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Olğun Y, Aksoy Poyraz C, Bozluolçay M, Poyraz BÇ. Quantitative EEG in the Differential Diagnosis of Dementia Subtypes. J Geriatr Psychiatry Neurol 2024; 37:368-378. [PMID: 38217438 DOI: 10.1177/08919887241227410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Abstract
OBJECTIVE Most neurodegenerative dementias present with substantial overlap in clinical features. Therefore, differential diagnosis is often a challenging task necessitating costly and sometimes invasive diagnostic procedures. A promising, non-invasive and cost-effective method is the widely available electroencephalography (EEG). METHODS Twenty-three subjects with Alzheimer's disease (AD), 28 subjects with dementia with Lewy bodies (DLB), 15 subjects with frontotemporal dementias (FTDs), and 22 healthy controls (HC) were enrolled. Nineteen channel computerized EEG recordings were acquired. Mean relative powers were calculated using the standard frequency bands. Theta/alpha ratio (TAR), theta/beta ratio (TBR), a spectral index of (alpha + beta)/(theta + delta) and an alpha reactivity index (alpha in eyes-open condition/alpha in eyes-closed condition) were also calculated. Receiver operating characteristic (ROC) analyses were performed to assess diagnostic accuracy. RESULTS For the comparison of EEG measures across groups, we performed a multivariate ANOVA followed by univariate ANOVAs controlling for the effects of age, with post hoc tests. Theta power and TBR were increased in DLB compared to other groups. Alpha power was decreased in DLB compared to HC and FTD; and in AD compared to FTD. Beta power was decreased in DLB compared to AD and HC. Furthermore, regional analyses demonstrated a unique pattern of theta power increase in DLB; affecting frontal, central, parietal, occipital, and temporal regions. In AD, theta power increased compared to HC in parietal, occipital, and right temporal regions. TAR was increased in DLB compared to other groups; and in AD compared to HC. Finally, alpha reactivity index was higher in DLB compared to HC and FTD. In AD, EEG slowing was associated with cognitive impairment, while in DLB, this was associated with higher DLB characteristics. In the ROC analyses to distinguish DLB from FTD and AD, measures of EEG slowing yielded high area under curve values, with good specificities. Also, decreased alpha reactivity could distinguish DLB from FTD with good specificity. EEG slowing in DLB showed a diffuse pattern compared to AD, where a posterior and temporal slowing predominated. CONCLUSION We showed that EEG slowing was satisfactory in distinguishing DLB patients from AD and FTD patients. Notably, this slowing was a characteristic finding in DLB patients, even at early stages, while it paralleled disease progression in AD. Furthermore, EEG slowing in DLB showed a diffuse pattern compared to AD, where a posterior and temporal slowing predominated. These findings align with the previous evidence of the diencephalic dysfunction in DLB.
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Affiliation(s)
- Yeşim Olğun
- Department of Psychiatry, Cerrahpaşa Medical School, Istanbul University-Cerrahpaşa, Istanbul, Turkey
| | - Cana Aksoy Poyraz
- Department of Psychiatry, Cerrahpaşa Medical School, Istanbul University-Cerrahpaşa, Istanbul, Turkey
| | - Melda Bozluolçay
- Department of Neurology, Cerrahpaşa Medical School, Istanbul University-Cerrahpaşa, Istanbul, Turkey
| | - Burç Çağrı Poyraz
- Department of Psychiatry, Cerrahpaşa Medical School, Istanbul University-Cerrahpaşa, Istanbul, Turkey
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García-Dopico N, Terrasa JL, González-Roldán AM, Velasco-Roldán O, Sitges C. Unraveling the Left-Right Judgment Task in Chronic Low Back Pain: Insights Through Behavioral, Electrophysiological, Motor Imagery, and Bodily Disruption Perspectives. THE JOURNAL OF PAIN 2024; 25:104484. [PMID: 38307439 DOI: 10.1016/j.jpain.2024.01.349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 01/24/2024] [Accepted: 01/25/2024] [Indexed: 02/04/2024]
Abstract
Bodily disruptions have been consistently demonstrated in individuals with chronic low back pain. The performance on the left-right judgment task has been purposed as an indirect measure of the cortical proprioceptive representation of the body. It has been suggested to be dependent on implicit motor imagery, although the available evidence is conflicting. Hence, the aim of this case-control observational study was to examine the performance (accuracy and reaction times) and event-related potentials while performing the left-right judgment task for back and hand images in individuals with chronic low back pain versus healthy controls, along with its relationship with self-reported measurements and quantitative sensory testing. While self-reported data suggested bodily disruptions in the chronic low back pain sample, this was not supported by quantitative sensory testing. Although both groups displayed the same performance, our results suggested an increased attentional load on participants with chronic low back pain to achieve equal performance, measured by a higher N1 peak amplitude in occipital electrodes, especially when the effect of contextual images arises. The absence of differences in the reaction times for the left-right judgment task between both groups, along with inconsistencies in self-reported and quantitative sensory testing data, could question the involvement of implicit motor imagery in solving the task. In conclusion, our results suggest disrupted attentional processing in participants with chronic low back pain to solve the left-right judgment task. PERSPECTIVE: Although there are no differences in the performance of the left-right judgment task (hits, reaction times) between chronic low back pain patients and controls, the analysis of event-related potentials revealed that patients require a higher cognitive load, measured by N1 peak amplitude.
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Affiliation(s)
- Nuria García-Dopico
- Department of Nursing and Physiotherapy, University of the Balearic Islands (UIB), Palma, Spain; Research Institute of Health Sciences (IUNICS), Health Research Institute of the Balearic Islands (IdISBa), Palma, Spain
| | - Juan L Terrasa
- Research Institute of Health Sciences (IUNICS), Health Research Institute of the Balearic Islands (IdISBa), Palma, Spain; Department of Psychology, University of the Balearic Islands (UIB), Palma, Spain
| | - Ana M González-Roldán
- Research Institute of Health Sciences (IUNICS), Health Research Institute of the Balearic Islands (IdISBa), Palma, Spain; Department of Psychology, University of the Balearic Islands (UIB), Palma, Spain
| | - Olga Velasco-Roldán
- Department of Nursing and Physiotherapy, University of the Balearic Islands (UIB), Palma, Spain; Research Institute of Health Sciences (IUNICS), Health Research Institute of the Balearic Islands (IdISBa), Palma, Spain
| | - Carolina Sitges
- Research Institute of Health Sciences (IUNICS), Health Research Institute of the Balearic Islands (IdISBa), Palma, Spain; Department of Psychology, University of the Balearic Islands (UIB), Palma, Spain
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Sabater-Gárriz Á, Montoya P, Riquelme I. Enhanced EEG power density during painful stretching in individuals with cerebral palsy. RESEARCH IN DEVELOPMENTAL DISABILITIES 2024; 150:104760. [PMID: 38795555 DOI: 10.1016/j.ridd.2024.104760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 04/22/2024] [Accepted: 05/15/2024] [Indexed: 05/28/2024]
Abstract
BACKGROUND Pain perception mechanisms in cerebral palsy remain largely unclear. AIMS This study investigates brain activity in adults with cerebral palsy during painful and non-painful stretching to elucidate their pain processing characteristics. METHODS AND PROCEDURES Twenty adults with cerebral palsy and 20 controls underwent EEG in three conditions: rest, non-painful stretching, and painful stretching. Time-frequency power density of theta, alpha, and beta waves in somatosensory and frontal cortices was analyzed, alongside baseline pressure pain thresholds. OUTCOMES AND RESULTS Cerebral palsy individuals exhibited higher theta, alpha, and beta power density in both cortices during painful stretching compared to rest, and lower during non-painful stretching. Controls showed higher power density during non-painful stretching but lower during painful stretching. Cerebral palsy individuals had higher pain sensitivity, with those more sensitive experiencing greater alpha power density. CONCLUSIONS AND IMPLICATIONS These findings confirm alterations in the cerebral processing of pain in individuals with cerebral palsy. This knowledge could enhance future approaches to the diagnosis and treatment of pain in this vulnerable population.
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Affiliation(s)
- Álvaro Sabater-Gárriz
- Balearic ASPACE Foundation, Marratxí, Spain; Health Research Institute of the Balearic Islands (IUNICS-IdISBa), University of the Balearic Islands, Palma de Mallorca, Spain; Department of Nursing and Physiotherapy, University of the Balearic Islands, Palma de Mallorca, Spain
| | - Pedro Montoya
- Health Research Institute of the Balearic Islands (IUNICS-IdISBa), University of the Balearic Islands, Palma de Mallorca, Spain; Center for Mathematics, Computation and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Inmaculada Riquelme
- Health Research Institute of the Balearic Islands (IUNICS-IdISBa), University of the Balearic Islands, Palma de Mallorca, Spain; Department of Nursing and Physiotherapy, University of the Balearic Islands, Palma de Mallorca, Spain.
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Pfeiffer M, Kübler A, Hilger K. Modulation of human frontal midline theta by neurofeedback: A systematic review and quantitative meta-analysis. Neurosci Biobehav Rev 2024; 162:105696. [PMID: 38723734 DOI: 10.1016/j.neubiorev.2024.105696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 03/27/2024] [Accepted: 04/26/2024] [Indexed: 05/19/2024]
Abstract
Human brain activity consists of different frequency bands associated with varying functions. Oscillatory activity of frontal brain regions in the theta range (4-8 Hz) is linked to cognitive processing and can be modulated by neurofeedback - a technique where participants receive real-time feedback about their brain activity and learn to modulate it. However, criticism of this technique evolved, and high heterogeneity of study designs complicates a valid evaluation of its effectiveness. This meta-analysis provides the first systematic overview over studies attempting to modulate frontal midline theta with neurofeedback in healthy human participants. Out of 1261 articles screened, 14 studies were eligible for systematic review and 11 for quantitative meta-analyses. Studies were evaluated following the DIAD model and the PRISMA guidelines. A significant across-study effect of medium size (Hedges' g = .66; 95%-CI [-0.62, 1.73]) with substantial between-study heterogeneity (Q(16) = 167.43, p < .001) was observed and subanalysis revealed effective frontal midline theta upregulation. We discuss moderators of effect sizes and provide guidelines for future research in this dynamic field.
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Affiliation(s)
- Maria Pfeiffer
- Institute of Psychology, Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D-97070, Germany
| | - Andrea Kübler
- Institute of Psychology, Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D-97070, Germany
| | - Kirsten Hilger
- Institute of Psychology, Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D-97070, Germany.
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Luo X, Zhou B, Fang J, Cherif-Riahi Y, Li G, Shen X. Integrating EEG and Ensemble Learning for Accurate Grading and Quantification of Generalized Anxiety Disorder: A Novel Diagnostic Approach. Diagnostics (Basel) 2024; 14:1122. [PMID: 38893648 PMCID: PMC11172130 DOI: 10.3390/diagnostics14111122] [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: 04/06/2024] [Revised: 05/23/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
Current assessments for generalized anxiety disorder (GAD) are often subjective and do not rely on a standardized measure to evaluate the GAD across its severity levels. The lack of objective and multi-level quantitative diagnostic criteria poses as a significant challenge for individualized treatment strategies. To address this need, this study aims to establish a GAD grading and quantification diagnostic model by integrating an electroencephalogram (EEG) and ensemble learning. In this context, a total of 39 normal subjects and 80 GAD patients were recruited and divided into four groups: normal control, mild GAD, moderate GAD, and severe GAD. Ten minutes resting state EEG data were collected for every subject. Functional connectivity features were extracted from each EEG segment with different time windows. Then, ensemble learning was employed for GAD classification studies and brain mechanism analysis. Hence, the results showed that the Catboost model with a 10 s time window achieved an impressive 98.1% accuracy for four-level classification. Particularly, it was found that those functional connections situated between the frontal and temporal lobes were significantly more abundant than in other regions, with the beta rhythm being the most prominent. The analysis framework and findings of this study provide substantial evidence for the applications of artificial intelligence in the clinical diagnosis of GAD.
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Affiliation(s)
- Xiaodong Luo
- The Second Hospital of Jinhua, Jinhua 321016, China;
| | - Bin Zhou
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China;
| | - Jiaqi Fang
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China;
| | - Yassine Cherif-Riahi
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China;
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China;
| | - Xueqian Shen
- The Second Hospital of Jinhua, Jinhua 321016, China;
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Chen S, Zhang C, Yang H, Peng L, Xie H, Lv Z, Hou ZG. A Multi-Modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease Using Three Paradigms With Various Task Difficulties. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1477-1486. [PMID: 38568773 DOI: 10.1109/tnsre.2024.3379891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
Alzheimer's Disease (AD) accounts for the majority of dementia, and Mild Cognitive Impairment (MCI) is the early stage of AD. Early and accurate diagnosis of dementia plays a vital role in more targeted treatments and effectively halting disease progression. However, the clinical diagnosis of dementia requires various examinations, which are expensive and require a high level of expertise from the doctor. In this paper, we proposed a classification method based on multi-modal data including Electroencephalogram (EEG), eye tracking and behavioral data for early diagnosis of AD and MCI. Paradigms with various task difficulties were used to identify different severity of dementia: eye movement task and resting-state EEG tasks were used to detect AD, while eye movement task and delayed match-to-sample task were used to detect MCI. Besides, the effects of different features were compared and suitable EEG channels were selected for the detection. Furthermore, we proposed a data augmentation method to enlarge the dataset, designed an extra ERPNet feature extract layer to extract multi-modal features and used domain-adversarial neural network to improve the performance of MCI diagnosis. We achieved an average accuracy of 88.81% for MCI diagnosis and 100% for AD diagnosis. The results of this paper suggest that our classification method can provide a feasible and affordable way to diagnose dementia.
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Cataldo A, Criscuolo S, De Benedetto E, Masciullo A, Pesola M, Schiavoni R. A Novel Metric for Alzheimer's Disease Detection Based on Brain Complexity Analysis via Multiscale Fuzzy Entropy. Bioengineering (Basel) 2024; 11:324. [PMID: 38671746 PMCID: PMC11048692 DOI: 10.3390/bioengineering11040324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative brain disorder that affects cognitive functioning and memory. Current diagnostic tools, including neuroimaging techniques and cognitive questionnaires, present limitations such as invasiveness, high costs, and subjectivity. In recent years, interest has grown in using electroencephalography (EEG) for AD detection due to its non-invasiveness, low cost, and high temporal resolution. In this regard, this work introduces a novel metric for AD detection by using multiscale fuzzy entropy (MFE) to assess brain complexity, offering clinicians an objective, cost-effective diagnostic tool to aid early intervention and patient care. To this purpose, brain entropy patterns in different frequency bands for 35 healthy subjects (HS) and 35 AD patients were investigated. Then, based on the resulting MFE values, a specific detection algorithm, able to assess brain complexity abnormalities that are typical of AD, was developed and further validated on 24 EEG test recordings. This MFE-based method achieved an accuracy of 83% in differentiating between HS and AD, with a diagnostic odds ratio of 25, and a Matthews correlation coefficient of 0.67, indicating its viability for AD diagnosis. Furthermore, the algorithm showed potential for identifying anomalies in brain complexity when tested on a subject with mild cognitive impairment (MCI), warranting further investigation in future research.
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Affiliation(s)
- Andrea Cataldo
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.M.); (R.S.)
| | - Sabatina Criscuolo
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy; (S.C.); (E.D.B.); (M.P.)
| | - Egidio De Benedetto
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy; (S.C.); (E.D.B.); (M.P.)
| | - Antonio Masciullo
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.M.); (R.S.)
| | - Marisa Pesola
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy; (S.C.); (E.D.B.); (M.P.)
| | - Raissa Schiavoni
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.M.); (R.S.)
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Ribeiro LDJA, Bastos VHDV, Coertjens M. Breath-holding as model for the evaluation of EEG signal during respiratory distress. Eur J Appl Physiol 2024; 124:753-760. [PMID: 38105311 DOI: 10.1007/s00421-023-05379-x] [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: 08/08/2023] [Accepted: 11/14/2023] [Indexed: 12/19/2023]
Abstract
PURPOSE Research describes the existence of a relationship between cortical activity and the regulation of bulbar respiratory centers through the evaluation of the electroencephalographic (EEG) signal during respiratory challenges. For example, we found evidences of a reduction in the frequency of the EEG (alpha band) in both divers and non-divers during apnea tests. For instance, this reduction was more prominent in divers due to the greater physiological disturbance resulting from longer apnea time. However, little is known about EEG adaptations during tests of maximal apnea, a test that voluntarily stops breathing and induces dyspnea. RESULTS Through this mini-review, we verified that a protocol of successive apneas triggers a significant increase in the maximum apnea time and we hypothesized that successive maximal apnea test could be a powerful model for the study of cortical activity during respiratory distress. CONCLUSION Dyspnea is a multifactorial symptom and we believe that performing a successive maximal apnea protocol is possible to understand some factors that determine the sensation of dyspnea through the EEG signal, especially in people not trained in apnea.
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Affiliation(s)
- Lucas de Jesus Alves Ribeiro
- Physiotherapy Department, Universidade Federal do Delta do Parnaíba, Av. São Sebastião, CEP: 64.202-020, Parnaíba, PI, 2819, Brazil
- Brain Mapping and Functionality Laboratory, Universidade Federal do Delta do Parnaíba, Piauí, Brazil
| | - Victor Hugo do Vale Bastos
- Physiotherapy Department, Universidade Federal do Delta do Parnaíba, Av. São Sebastião, CEP: 64.202-020, Parnaíba, PI, 2819, Brazil
- Postgraduate Program in Biomedical Sciences, Universidade Federal do Delta do Parnaíba, Piauí, Brazil
- Brain Mapping and Functionality Laboratory, Universidade Federal do Delta do Parnaíba, Piauí, Brazil
| | - Marcelo Coertjens
- Physiotherapy Department, Universidade Federal do Delta do Parnaíba, Av. São Sebastião, CEP: 64.202-020, Parnaíba, PI, 2819, Brazil.
- Postgraduate Program in Biomedical Sciences, Universidade Federal do Delta do Parnaíba, Piauí, Brazil.
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Abbasi H, Davidson JO, Dhillon SK, Zhou KQ, Wassink G, Gunn AJ, Bennet L. Deep Learning for Generalized EEG Seizure Detection after Hypoxia-Ischemia-Preclinical Validation. Bioengineering (Basel) 2024; 11:217. [PMID: 38534490 DOI: 10.3390/bioengineering11030217] [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: 01/29/2024] [Revised: 02/12/2024] [Accepted: 02/23/2024] [Indexed: 03/28/2024] Open
Abstract
Brain maturity and many clinical treatments such as therapeutic hypothermia (TH) can significantly influence the morphology of neonatal EEG seizures after hypoxia-ischemia (HI), and so there is a need for generalized automatic seizure identification. This study validates efficacy of advanced deep-learning pattern classifiers based on a convolutional neural network (CNN) for seizure detection after HI in fetal sheep and determines the effects of maturation and brain cooling on their accuracy. The cohorts included HI-normothermia term (n = 7), HI-hypothermia term (n = 14), sham-normothermia term (n = 5), and HI-normothermia preterm (n = 14) groups, with a total of >17,300 h of recordings. Algorithms were trained and tested using leave-one-out cross-validation and k-fold cross-validation approaches. The accuracy of the term-trained seizure detectors was consistently excellent for HI-normothermia preterm data (accuracy = 99.5%, area under curve (AUC) = 99.2%). Conversely, when the HI-normothermia preterm data were used in training, the performance on HI-normothermia term and HI-hypothermia term data fell (accuracy = 98.6%, AUC = 96.5% and accuracy = 96.9%, AUC = 89.6%, respectively). Findings suggest that HI-normothermia preterm seizures do not contain all the spectral features seen at term. Nevertheless, an average 5-fold cross-validated accuracy of 99.7% (AUC = 99.4%) was achieved from all seizure detectors. This significant advancement highlights the reliability of the proposed deep-learning algorithms in identifying clinically translatable post-HI stereotypic seizures in 256Hz recordings, regardless of maturity and with minimal impact from hypothermia.
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Affiliation(s)
- Hamid Abbasi
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
- Auckland Bioengineering Institute (ABI), University of Auckland, Auckland 1010, New Zealand
| | - Joanne O Davidson
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Simerdeep K Dhillon
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Kelly Q Zhou
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Guido Wassink
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Alistair J Gunn
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Laura Bennet
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
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Ryalino C, Sahinovic MM, Drost G, Absalom AR. Intraoperative monitoring of the central and peripheral nervous systems: a narrative review. Br J Anaesth 2024; 132:285-299. [PMID: 38114354 DOI: 10.1016/j.bja.2023.11.032] [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: 05/08/2023] [Revised: 11/03/2023] [Accepted: 11/03/2023] [Indexed: 12/21/2023] Open
Abstract
The central and peripheral nervous systems are the primary target organs during anaesthesia. At the time of the inception of the British Journal of Anaesthesia, monitoring of the central nervous system comprised clinical observation, which provided only limited information. During the 100 yr since then, and particularly in the past few decades, significant progress has been made, providing anaesthetists with tools to obtain real-time assessments of cerebral neurophysiology during surgical procedures. In this narrative review article, we discuss the rationale and uses of electroencephalography, evoked potentials, near-infrared spectroscopy, and transcranial Doppler ultrasonography for intraoperative monitoring of the central and peripheral nervous systems.
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Affiliation(s)
- Christopher Ryalino
- Department of Anaesthesiology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Marko M Sahinovic
- Department of Anaesthesiology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Gea Drost
- Department of Neurology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands; Department of Neurosurgery, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Anthony R Absalom
- Department of Anaesthesiology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands.
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Mao X, Zhang Z, Yang Y, Chen Y, Wang Y, Wang W. Characteristics of different Mandarin pronunciation element perception: evidence based on a multifeature paradigm for recording MMN and P3a components of phonemic changes in speech sounds. Front Neurosci 2024; 17:1277129. [PMID: 38264493 PMCID: PMC10804857 DOI: 10.3389/fnins.2023.1277129] [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: 08/14/2023] [Accepted: 12/18/2023] [Indexed: 01/25/2024] Open
Abstract
Background As a tonal language, Mandarin Chinese has the following pronunciation elements for each syllable: the vowel, consonant, tone, duration, and intensity. Revealing the characteristics of auditory-related cortical processing of these different pronunciation elements is interesting. Methods A Mandarin pronunciation multifeature paradigm was designed, during which a standard stimulus and five different phonemic deviant stimuli were presented. The electroencephalogram (EEG) data were recorded with 256-electrode high-density EEG equipment. Time-domain and source localization analyses were conducted to demonstrate waveform characteristics and locate the sources of the cortical processing of mismatch negativity (MMN) and P3a components following different stimuli. Results Vowel and consonant differences elicited distinct MMN and P3a components, but tone and duration differences did not. Intensity differences elicited distinct MMN components but not P3a components. For MMN and P3a components, the activated cortical areas were mainly in the frontal-temporal lobe. However, the regions and intensities of the cortical activation were significantly different among the components for the various deviant stimuli. The activated cortical areas of the MMN and P3a components elicited by vowels and consonants seemed to be larger and show more intense activation. Conclusion The auditory processing centers use different auditory-related cognitive resources when processing different Mandarin pronunciation elements. Vowels and consonants carry more information for speech comprehension; moreover, more neurons in the cortex may be involved in the recognition and cognitive processing of these elements.
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Affiliation(s)
- Xiang Mao
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
| | - Ziyue Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
| | - Yijing Yang
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
| | - Yu Chen
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
| | - Yue Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
| | - Wei Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China
- Institute of Otolaryngology of Tianjin, Tianjin, China
- Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China
- Key Medical Discipline of Tianjin (Otolaryngology), Tianjin, China
- Otolaryngology Clinical Quality Control Centre, Tianjin, China
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Pilcevic D, Djuric Jovicic M, Antonijevic M, Bacanin N, Jovanovic L, Zivkovic M, Dragovic M, Bisevac P. Performance evaluation of metaheuristics-tuned recurrent neural networks for electroencephalography anomaly detection. Front Physiol 2023; 14:1267011. [PMID: 38033337 PMCID: PMC10682794 DOI: 10.3389/fphys.2023.1267011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 10/26/2023] [Indexed: 12/02/2023] Open
Abstract
Electroencephalography (EEG) serves as a diagnostic technique for measuring brain waves and brain activity. Despite its precision in capturing brain electrical activity, certain factors like environmental influences during the test can affect the objectivity and accuracy of EEG interpretations. Challenges associated with interpretation, even with advanced techniques to minimize artifact influences, can significantly impact the accurate interpretation of EEG findings. To address this issue, artificial intelligence (AI) has been utilized in this study to analyze anomalies in EEG signals for epilepsy detection. Recurrent neural networks (RNNs) are AI techniques specifically designed to handle sequential data, making them well-suited for precise time-series tasks. While AI methods, including RNNs and artificial neural networks (ANNs), hold great promise, their effectiveness heavily relies on the initial values assigned to hyperparameters, which are crucial for their performance for concrete assignment. To tune RNN performance, the selection of hyperparameters is approached as a typical optimization problem, and metaheuristic algorithms are employed to further enhance the process. The modified hybrid sine cosine algorithm has been developed and used to further improve hyperparameter optimization. To facilitate testing, publicly available real-world EEG data is utilized. A dataset is constructed using captured data from healthy and archived data from patients confirmed to be affected by epilepsy, as well as data captured during an active seizure. Two experiments have been conducted using generated dataset. In the first experiment, models were tasked with the detection of anomalous EEG activity. The second experiment required models to segment normal, anomalous activity as well as detect occurrences of seizures from EEG data. Considering the modest sample size (one second of data, 158 data points) used for classification models demonstrated decent outcomes. Obtained outcomes are compared with those generated by other cutting-edge metaheuristics and rigid statistical validation, as well as results' interpretation is performed.
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Affiliation(s)
- Dejan Pilcevic
- Clinic for Nephrology, Military Medical Academy, University of Defense, Belgrade, Serbia
| | | | - Milos Antonijevic
- Department of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Nebojsa Bacanin
- Department of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Luka Jovanovic
- Department of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Miodrag Zivkovic
- Department of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | | | - Petar Bisevac
- Department of Informatics and Computing, Singidunum University, Belgrade, Serbia
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13
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Zilinskaite N, Shukla RP, Baradoke A. Use of 3D Printing Techniques to Fabricate Implantable Microelectrodes for Electrochemical Detection of Biomarkers in the Early Diagnosis of Cardiovascular and Neurodegenerative Diseases. ACS MEASUREMENT SCIENCE AU 2023; 3:315-336. [PMID: 37868357 PMCID: PMC10588936 DOI: 10.1021/acsmeasuresciau.3c00028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/25/2023] [Accepted: 08/25/2023] [Indexed: 10/24/2023]
Abstract
This Review provides a comprehensive overview of 3D printing techniques to fabricate implantable microelectrodes for the electrochemical detection of biomarkers in the early diagnosis of cardiovascular and neurodegenerative diseases. Early diagnosis of these diseases is crucial to improving patient outcomes and reducing healthcare systems' burden. Biomarkers serve as measurable indicators of these diseases, and implantable microelectrodes offer a promising tool for their electrochemical detection. Here, we discuss various 3D printing techniques, including stereolithography (SLA), digital light processing (DLP), fused deposition modeling (FDM), selective laser sintering (SLS), and two-photon polymerization (2PP), highlighting their advantages and limitations in microelectrode fabrication. We also explore the materials used in constructing implantable microelectrodes, emphasizing their biocompatibility and biodegradation properties. The principles of electrochemical detection and the types of sensors utilized are examined, with a focus on their applications in detecting biomarkers for cardiovascular and neurodegenerative diseases. Finally, we address the current challenges and future perspectives in the field of 3D-printed implantable microelectrodes, emphasizing their potential for improving early diagnosis and personalized treatment strategies.
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Affiliation(s)
- Nemira Zilinskaite
- Wellcome/Cancer
Research UK Gurdon Institute, Henry Wellcome Building of Cancer and
Developmental Biology, University of Cambridge, Tennis Court Road, Cambridge CB2 1QN, U.K.
- Faculty
of Medicine, University of Vilnius, M. K. Čiurlionio g. 21, LT-03101 Vilnius, Lithuania
| | - Rajendra P. Shukla
- BIOS
Lab-on-a-Chip Group, MESA+ Institute for Nanotechnology, Max Planck
Center for Complex Fluid Dynamics, University
of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
| | - Ausra Baradoke
- Wellcome/Cancer
Research UK Gurdon Institute, Henry Wellcome Building of Cancer and
Developmental Biology, University of Cambridge, Tennis Court Road, Cambridge CB2 1QN, U.K.
- Faculty
of Medicine, University of Vilnius, M. K. Čiurlionio g. 21, LT-03101 Vilnius, Lithuania
- BIOS
Lab-on-a-Chip Group, MESA+ Institute for Nanotechnology, Max Planck
Center for Complex Fluid Dynamics, University
of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
- Center for
Physical Sciences and Technology, Savanoriu 231, LT-02300 Vilnius, Lithuania
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14
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Choi SO, Choi JG, Yun JY. A Study of Brain Function Characteristics of Service Members at High Risk for Accidents in the Military. Brain Sci 2023; 13:1157. [PMID: 37626513 PMCID: PMC10452066 DOI: 10.3390/brainsci13081157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 07/23/2023] [Accepted: 07/29/2023] [Indexed: 08/27/2023] Open
Abstract
Military accidents are often associated with stress and depressive psychological conditions among soldiers, and they often fail to adapt to military life. Therefore, this study analyzes whether there are differences in EEG and pulse wave indices between general soldiers and three groups of soldiers who have not adapted to military life and are at risk of accidents. Data collection was carried out using a questionnaire and a device that can measure EEG and pulse waves, and data analysis was performed using SPSS. The results showed that the concentration level and brain activity indices were higher in the general soldiers and the soldiers in the first stage of accident risk. The body stress index was higher for each stage of accident risk, and the physical vitality index was higher for general soldiers. Therefore, it can be seen that soldiers who have not adapted to military life and are at risk of accidents have somewhat lower concentration and brain activity than general soldiers, and have symptoms of stress and lethargy. The results of this study will contribute to reducing human accidents through EEG and pulse wave measurements not only in the military but also in occupations with a high risk of accidents such as construction.
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Affiliation(s)
| | | | - Jong-Yong Yun
- Department of Protection and Safety Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
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15
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Singh A, Velagala VR, Kumar T, Dutta RR, Sontakke T. The Application of Deep Learning to Electroencephalograms, Magnetic Resonance Imaging, and Implants for the Detection of Epileptic Seizures: A Narrative Review. Cureus 2023; 15:e42460. [PMID: 37637568 PMCID: PMC10457132 DOI: 10.7759/cureus.42460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 07/25/2023] [Indexed: 08/29/2023] Open
Abstract
Epilepsy is a neurological disorder characterized by recurrent seizures affecting millions worldwide. Medically intractable seizures in epilepsy patients are not only detrimental to the quality of life but also pose a significant threat to their safety. Outcomes of epilepsy therapy can be improved by early detection and intervention during the interictal window period. Electroencephalography is the primary diagnostic tool for epilepsy, but accurate interpretation of seizure activity is challenging and highly time-consuming. Machine learning (ML) and deep learning (DL) algorithms enable us to analyze complex EEG data, which can not only help us diagnose but also locate epileptogenic zones and predict medical and surgical treatment outcomes. DL models such as convolutional neural networks (CNNs), inspired by visual processing, can be used to classify EEG activity. By applying preprocessing techniques, signal quality can be enhanced by denoising and artifact removal. DL can also be incorporated into the analysis of magnetic resonance imaging (MRI) data, which can help in the localization of epileptogenic zones in the brain. Proper detection of these zones can help in good neurosurgical outcomes. Recent advancements in DL have facilitated the implementation of these systems in neural implants and wearable devices, allowing for real-time seizure detection. This has the potential to transform the management of drug-refractory epilepsy. This review explores the application of ML and DL techniques to Electroencephalograms (EEGs), MRI, and wearable devices for epileptic seizure detection. This review briefly explains the fundamentals of both artificial intelligence (AI) and DL, highlighting these systems' potential advantages and undeniable limitations.
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Affiliation(s)
- Arihant Singh
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Vivek R Velagala
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Tanishq Kumar
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Rajoshee R Dutta
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Tushar Sontakke
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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16
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Wu Q, Jiang H, Shao C, Zhang Y, Zhou W, Cao Y, Song J, Shi B, Chi A, Wang C. Characteristics of changes in the functional status of the brain before and after 1,000 m all-out paddling for different levels of dragon boat athletes. Front Psychol 2023; 14:1109949. [PMID: 37287781 PMCID: PMC10243504 DOI: 10.3389/fpsyg.2023.1109949] [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: 12/01/2022] [Accepted: 03/31/2023] [Indexed: 06/09/2023] Open
Abstract
Purposes Dragon boat is a traditional sport in China, but the brain function characteristics of dragon boat athletes are still unclear. Our purpose is to explore the changing characteristics of brain function of dragon boat athletes at different levels before and after exercise by monitoring the changes of EEG power spectrum and microstate of athletes before and after rowing. Methods Twenty-four expert dragon boat athletes and 25 novice dragon boat athletes were selected as test subjects to perform the 1,000 m all-out paddling exercise on a dragon boat dynamometer. Their resting EEG data was collected pre- and post-exercise, and the EEG data was pre-processed and then analyzed using power spectrum and microstate based on Matlab software. Results Post-Exercise, the Heart Rate peak (HR peak), Percentage of Heart Rate max (HR max), Rating of Perceived Exertion (RPE), and Exercise duration of the novice group were significantly higher than expert group (p < 0.01). Pre-exercise, the power spectral density values in the δ, α1, α2, and β1 bands were significantly higher in the expert group compared to the novice group (p < 0.05). Post-exercise, the power spectral density values in the δ, θ, and α1 bands were significantly lower in the expert group compared to the novice group (p < 0.05), the power spectral density values of α2, β1, and β2 bands were significantly higher (p < 0.05). The results of microstate analysis showed that the duration and contribution of microstate class D were significantly higher in the pre-exercise expert group compared to the novice group (p < 0.05), the transition probabilities of A → D, C → D, and D → A were significantly higher (p < 0.05). Post-exercise, the duration, and contribution of microstate class C in the expert group decreased significantly compared to the novice group (p < 0.05), the occurrence of microstate classes A and D were significantly higher (p < 0.05), the transition probability of A → B was significantly higher (p < 0.05), and the transition probabilities of C → D and D → C were significantly lower (p < 0.05). Conclusion The functional brain state of dragon boat athletes was characterized by expert athletes with closer synaptic connections of brain neurons and higher activation of the dorsal attention network in the resting state pre-exercise. There still had higher activation of cortical neurons after paddling exercise. Expert athletes can better adapt to acute full-speed oar training.
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Affiliation(s)
- Qianqian Wu
- School of Sports, Shaanxi Normal University, Xi’ an, China
| | - Hongke Jiang
- Physical Education Department, Shanghai Maritime University, Shanghai, China
| | - Changzhuan Shao
- Physical Education Department, Shanghai Maritime University, Shanghai, China
| | - Yan Zhang
- School of Sports, Shaanxi Normal University, Xi’ an, China
| | - Wu Zhou
- School of Sports, Shaanxi Normal University, Xi’ an, China
| | - Yingying Cao
- School of Sports, Shaanxi Normal University, Xi’ an, China
| | - Jing Song
- School of Sports, Shaanxi Normal University, Xi’ an, China
| | - Bing Shi
- School of Sports, Shaanxi Normal University, Xi’ an, China
| | - Aiping Chi
- School of Sports, Shaanxi Normal University, Xi’ an, China
| | - Chao Wang
- School of Sports, Shaanxi Normal University, Xi’ an, China
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17
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Zhang JJ, Sánchez Vidaña DI, Chan JNM, Hui ESK, Lau KK, Wang X, Lau BWM, Fong KNK. Biomarkers for prognostic functional recovery poststroke: A narrative review. Front Cell Dev Biol 2023; 10:1062807. [PMID: 36699006 PMCID: PMC9868572 DOI: 10.3389/fcell.2022.1062807] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
Background and objective: Prediction of poststroke recovery can be expressed by prognostic biomarkers that are related to the pathophysiology of stroke at the cellular and molecular level as well as to the brain structural and functional reserve after stroke at the systems neuroscience level. This study aimed to review potential biomarkers that can predict poststroke functional recovery. Methods: A narrative review was conducted to qualitatively summarize the current evidence on biomarkers used to predict poststroke functional recovery. Results: Neurophysiological measurements and neuroimaging of the brain and a wide diversity of molecules had been used as prognostic biomarkers to predict stroke recovery. Neurophysiological studies using resting-state electroencephalography (EEG) revealed an interhemispheric asymmetry, driven by an increase in low-frequency oscillation and a decrease in high-frequency oscillation in the ipsilesional hemisphere relative to the contralesional side, which was indicative of individual recovery potential. The magnitude of somatosensory evoked potentials and event-related desynchronization elicited by movement in task-related EEG was positively associated with the quantity of recovery. Besides, transcranial magnetic stimulation (TMS) studies revealed the potential values of using motor-evoked potentials (MEP) and TMS-evoked EEG potentials from the ipsilesional motor cortex as prognostic biomarkers. Brain structures measured using magnetic resonance imaging (MRI) have been implicated in stroke outcome prediction. Specifically, the damage to the corticospinal tract (CST) and anatomical motor connections disrupted by stroke lesion predicted motor recovery. In addition, a wide variety of molecular, genetic, and epigenetic biomarkers, including hemostasis, inflammation, tissue remodeling, apoptosis, oxidative stress, infection, metabolism, brain-derived, neuroendocrine, and cardiac biomarkers, etc., were associated with poor functional outcomes after stroke. However, challenges such as mixed evidence and analytical concerns such as specificity and sensitivity have to be addressed before including molecular biomarkers in routine clinical practice. Conclusion: Potential biomarkers with prognostic values for the prediction of functional recovery after stroke have been identified; however, a multimodal approach of biomarkers for prognostic prediction has rarely been studied in the literature. Future studies may incorporate a combination of multiple biomarkers from big data and develop algorithms using data mining methods to predict the recovery potential of patients after stroke in a more precise way.
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Affiliation(s)
- Jack Jiaqi Zhang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | | | - Jackie Ngai-Man Chan
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Edward S. K. Hui
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
- Department of Psychiatry, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Kui Kai Lau
- Division of Neurology, Department of Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xin Wang
- Department of Rehabilitation Medicine, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Benson W. M. Lau
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Kenneth N. K. Fong
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
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18
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Notturno F, Croce P, Ornello R, Sacco S, Zappasodi F. Yield of EEG features as markers of disease severity in amyotrophic lateral sclerosis: a pilot study. Amyotroph Lateral Scler Frontotemporal Degener 2022; 24:295-303. [PMID: 37078278 DOI: 10.1080/21678421.2022.2152696] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
OBJECTIVE To clarify the role of electroencephalography (EEG) as a promising marker of severity in amyotrophic lateral sclerosis (ALS). We characterized the brain spatio-temporal patterns activity at rest by means of both spectral band powers and EEG microstates and correlated these features with clinical scores. METHODS Eyes closed EEG was acquired in 15 patients with ALS and spectral band power was calculated in frequency bands, defined on the basis of individual alpha frequency (IAF): delta-theta band (1-7 Hz); low alpha (IAF - 2 Hz - IAF); high alpha (IAF - IAF + 2 Hz); beta (13 - 25 Hz). EEG microstate metrics (duration, occurrence, and coverage) were also evaluated. Spectral band powers and microstate metrics were correlated with several clinical scores of disabilities and disease progression. As a control group, 15 healthy volunteers were enrolled. RESULTS The beta-band power in motor/frontal regions was higher in patients with higher disease burden, negatively correlated with clinical severity scores and positively correlated with disease progression. Overall microstate duration was longer and microstate occurrence was lower in patients than in controls. Longer duration was correlated with a worse clinical status. CONCLUSIONS Our results showed that beta-band power and microstate metrics may be good candidates of disease severity in ALS. Increased beta and longer microstate duration in clinically worse patients suggest a possible impairment of both motor and non-motor network activities to fast modify their status. This can be interpreted as an attempt in ALS patients to compensate the disability but resulting in an ineffective and probably maladaptive behavior.
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Affiliation(s)
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, University “Gabriele d’Annunzio” of Chieti-Pescara, Chieti, Italy
- Behavioral Imaging and Neural Dynamics Center, University “Gabriele d’Annunzio” of Chieti–Pescara, Chieti, Italy
| | - Raffaele Ornello
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy, and
| | - Simona Sacco
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy, and
| | - Filippo Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences, University “Gabriele d’Annunzio” of Chieti-Pescara, Chieti, Italy
- Behavioral Imaging and Neural Dynamics Center, University “Gabriele d’Annunzio” of Chieti–Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies, University “Gabriele d’Annunzio” of Chieti–Pescara, Chieti, Italy
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19
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Comparison of Electroencephalogram Power Spectrum Characteristics of Left and Right Dragon Boat Athletes after 1 km of Rowing. Brain Sci 2022; 12:brainsci12121621. [PMID: 36552080 PMCID: PMC9776062 DOI: 10.3390/brainsci12121621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/20/2022] [Accepted: 11/21/2022] [Indexed: 11/29/2022] Open
Abstract
Purpose: This study aimed to detect differences in post-exercise brain activity between the left and right paddlers due to exercise by analyzing the resting-state electroencephalogram (EEG) power spectrum before and after exercise. Methods: Twenty-one right paddlers and twenty-two left paddlers completed a 1 km all-out test on a dragon boat ergometer, and their heart rate and exercise time were recorded. EEG signals were collected from superficial brain layers before and after exercise; then, the EEG power spectrum was extracted and compared in different frequency bands. In addition, the degree of lateralization in each brain region was assessed by the asymmetry index. Results: There was no significant difference in the power spectrum values and asymmetry indices between the left and right paddlers before rowing (p ˃ 0.05). However, after rowing, the left-paddlers group had significantly higher spectral power values in θ and α bands than the right-paddlers group (p < 0.05), and brain lateralization in both groups of athletes occurred mainly in the ipsilateral hemisphere of the frontal and central regions. Conclusion: The 1 km of rowing induced more brain activation in the left paddlers, and both left and right paddlers showed functional aggregation of hemispheric lateralization.
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20
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Li M, Liu Y, Liu Y, Pu C, Yin R, Zeng Z, Deng L, Wang X. Resting-state EEG-based convolutional neural network for the diagnosis of depression and its severity. Front Physiol 2022; 13:956254. [PMID: 36299253 PMCID: PMC9589234 DOI: 10.3389/fphys.2022.956254] [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: 05/30/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose: The study aimed to assess the value of the resting-state electroencephalogram (EEG)-based convolutional neural network (CNN) method for the diagnosis of depression and its severity in order to better serve depressed patients and at-risk populations. Methods: In this study, we used the resting state EEG-based CNN to identify depression and evaluated its severity. The EEG data were collected from depressed patients and healthy people using the Nihon Kohden EEG-1200 system. Analytical processing of resting-state EEG data was performed using Python and MATLAB software applications. The questionnaire included the Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), Symptom Check-List-90 (SCL-90), and the Eysenck Personality Questionnaire (EPQ). Results: A total of 82 subjects were included in this study, with 41 in the depression group and 41 in the healthy control group. The area under the curve (AUC) of the resting-state EEG-based CNN in depression diagnosis was 0.74 (95%CI: 0.70–0.77) with an accuracy of 66.40%. In the depression group, the SDS, SAS, SCL-90 subscales, and N scores were significantly higher in the major depression group than those in the non-major depression group (p < 0.05). The AUC of the model in depression severity was 0.70 (95%CI: 0.65–0.75) with an accuracy of 66.93%. Correlation analysis revealed that major depression AI scores were significantly correlated with SAS scores (r = 0.508, p = 0.003) and SDS scores (r = 0.765, p < 0.001). Conclusion: Our model can accurately identify the depression-specific EEG signal in terms of depression diagnosis and severity identification. It would eventually provide new strategies for early diagnosis of depression and its severity.
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Affiliation(s)
- Mengqian Li
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yuan Liu
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yan Liu
- Second Clinical Medical College, Nanchang University, Nanchang, China
| | - Changqin Pu
- Queen Mary College, Nanchang University, Nanchang, China
| | - Ruocheng Yin
- Queen Mary College, Nanchang University, Nanchang, China
| | - Ziqiang Zeng
- Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, China
- School of Public Health, Nanchang University, Nanchang, China
| | - Libin Deng
- Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, China
- *Correspondence: Libin Deng, ; Xing Wang,
| | - Xing Wang
- School of Life Sciences, Nanchang University, Nanchang, China
- Clinical Medical Experiment Center, Nanchang University, Nanchang, China
- *Correspondence: Libin Deng, ; Xing Wang,
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21
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Park BS, Jin S, Kim WY, Kang DS, Choi YJ, Lee YS. Comparison of the effects of cranial electrotherapy stimulation and midazolam as preoperative treatment in geriatric patients: A CONSORT-compliant randomized controlled trial. Medicine (Baltimore) 2022; 101:e30336. [PMID: 36107590 PMCID: PMC9439722 DOI: 10.1097/md.0000000000030336] [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: 11/26/2022] Open
Abstract
BACKGROUND Although midazolam is widely administered as an anxiolytic premedication, it may cause over-sedation and hypoxia in geriatric patients. Cranial electrotherapy stimulation (CES) is a nonpharmacological device with anxiolytic effect. This study compared the effects of CES and midazolam as a preoperative treatment in geriatric patients. METHODS Eighty patients, under the age of 65 to 79 years, undergoing general anesthesia were randomly assigned into midazolam premedication group (M group, n = 40) or CES pretreatment group (CES group, n = 40). The patients in the M group were intramuscularly injected with midazolam (0.07 mg/kg) 30 minutes before receiving general anesthesia. The patients in the CES group received 20 minutes of CES pretreatment on the day before and on the morning of the surgery. RESULTS In the preoperative holding area, the anxiety score (P = .02) and the sedation score (P < .001) were significantly lower in the CES group compared with those in the M group. The oxygen saturations at the preoperative holding area and the operating room were significantly higher in the CES group than those in the M group (P < .001). CONCLUSION CES pretreatment relieved preoperative anxiety with less risk of over-sedation and respiratory depression than midazolam premedication in geriatric patients.
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Affiliation(s)
- Byeong Seon Park
- Department of Anesthesiology and Pain Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea
| | - Sejong Jin
- Department of Anesthesiology and Pain Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea
- Department of Neuroscience, Korea University College of Medicine, Seoul, Republic of Korea
| | - Woon Young Kim
- Department of Anesthesiology and Pain Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea
- *Correspondence: Woon Young Kim, Department of Anesthesiology and Pain Medicine, Korea University Ansan Hospital, 516 Gojan-Dong, Danwon-Gu, Ansan city, Kyunggi-Do, 15355, Republic of Korea (e-mail: )
| | - Da Som Kang
- Department of Anesthesiology and Pain Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea
| | - Yoon Ji Choi
- Department of Anesthesiology and Pain Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea
| | - Yoon Sook Lee
- Department of Anesthesiology and Pain Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Gyeonggi-do, Republic of Korea
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22
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Daud SNSS, Sudirman R. Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review. Ann Biomed Eng 2022; 50:1271-1291. [PMID: 35994164 DOI: 10.1007/s10439-022-03053-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/10/2022] [Indexed: 11/26/2022]
Abstract
Electroencephalography (EEG) is a diagnostic test that records and measures the electrical activity of the human brain. Research investigating human behaviors and conditions using EEG has increased from year to year. Therefore, an efficient approach is vital to process the EEG dataset to improve the output signal quality. The wavelet is one of the well-known approaches for processing the EEG signal in time-frequency domain analysis. The wavelet is better than the traditional Fourier Transform because it has good time-frequency localized properties and multi-resolution analysis where the transient information of an EEG signal can be extracted efficiently. Thus, this review article aims to comprehensively describe the application of the wavelet method in denoising the EEG signal based on recent research. This review begins with a brief overview of the basic theory and characteristics of EEG and the wavelet transform method. Then, several wavelet-based methods commonly applied in EEG dataset denoising are described and a considerable number of the latest published EEG research works with wavelet applications are reviewed. Besides, the challenges that exist in current EEG-based wavelet method research are discussed. Finally, alternative solutions to mitigate the issues are recommended.
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Affiliation(s)
| | - Rubita Sudirman
- School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, 81310, Johor, Malaysia
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Ru X, He K, Lyu B, Li D, Xu W, Gu W, Ma X, Liu J, Li C, Li T, Zheng F, Yan X, Yin Y, Duan H, Na S, Wan S, Qin J, Sheng J, Gao JH. Multimodal neuroimaging with optically pumped magnetometers: A simultaneous MEG-EEG-fNIRS acquisition system. Neuroimage 2022; 259:119420. [PMID: 35777634 DOI: 10.1016/j.neuroimage.2022.119420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/13/2022] [Accepted: 06/27/2022] [Indexed: 11/24/2022] Open
Abstract
Multimodal neuroimaging plays an important role in neuroscience research. Integrated noninvasive neuroimaging modalities, such as magnetoencephalography (MEG), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), allow neural activity and related physiological processes in the brain to be precisely and comprehensively depicted, providing an effective and advanced platform to study brain function. Noncryogenic optically pumped magnetometer (OPM) MEG has high signal power due to its on-scalp sensor layout and enables more flexible configurations than traditional commercial superconducting MEG. Here, we integrate OPM-MEG with EEG and fNIRS to develop a multimodal neuroimaging system that can simultaneously measure brain electrophysiology and hemodynamics. We conducted a series of experiments to demonstrate the feasibility and robustness of our MEG-EEG-fNIRS acquisition system. The complementary neural and physiological signals simultaneously collected by our multimodal imaging system provide opportunities for a wide range of potential applications in neurovascular coupling, wearable neuroimaging, hyperscanning and brain-computer interfaces.
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Affiliation(s)
- Xingyu Ru
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Kaiyan He
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | | | - Dongxu Li
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Wei Xu
- Changping Laboratory, Beijing, China
| | - Wenyu Gu
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Xiao Ma
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Jiayi Liu
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | | | - Tingyue Li
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Fufu Zheng
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Xiaozhou Yan
- Beijing PsycheArk Science & Technology Development Co., Ltd., Beijing, China
| | - Yugang Yin
- Beijing PsycheArk Science & Technology Development Co., Ltd., Beijing, China
| | - Hongfeng Duan
- Beijing PsycheArk Science & Technology Development Co., Ltd., Beijing, China
| | - Shuai Na
- National Biomedical Imaging Center, Peking University, Beijing, China
| | - Shuangai Wan
- Beijing Automation Control Equipment Institute, Beijing, China
| | - Jie Qin
- Beijing Automation Control Equipment Institute, Beijing, China
| | | | - Jia-Hong Gao
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China; Changping Laboratory, Beijing, China; National Biomedical Imaging Center, Peking University, Beijing, China; McGovern Institute for Brain Research, Peking University, Beijing, China; Center for MRI Research, Academy for Advance Interdisciplinary Studies, Peking University, Beijing, China.
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Yang X, Li X, Yuan Y, Sun T, Yang J, Deng B, Yu H, Gao A, Guan J. 40 Hz Blue LED Relieves the Gamma Oscillations Changes Caused by Traumatic Brain Injury in Rat. Front Neurol 2022; 13:882991. [PMID: 35800078 PMCID: PMC9253286 DOI: 10.3389/fneur.2022.882991] [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: 02/24/2022] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundPhotobiomodulation (PBM) using low-level light-emitting diodes (LEDs) can be rapidly applied to various neurological disorders safely and non-invasively.Materials and MethodsForty-eight rats were involved in this study. The traumatic brain injury (TBI) model of rat was set up by a controlled cortical impact (CCI) injury. An 8-channel cortex electrode EEG was fixed to two hemispheres, and gamma oscillations were extracted according to each electrode. A 40 hz blue LED stimulation was set at four points of the frontal and parietal regions for 60 s each, six times per day for 1 week. Modified Neurologic Severity Scores (mNSS) were used to evaluate the level of neurological function.ResultsIn the right-side TBI model, the gamma oscillation decreased in electrodes Fp2, T4, C4, and O2; but significantly increased after 1 week of 40 hz Blue LED intervention. In the left-side TBI model, the gamma oscillation decreased in electrodes Fp1, T3, C3, and O1; and similarly increased after 1 week of 40 hz Blue LED intervention. Both left and right side TBI rats performed significantly better in mNSS after 40 hz Blue LED intervention.ConclusionTBI causes the decrease of gamma oscillations on the injured side of the brain of rats. The 40 hz Blue LED therapy could relieve the gamma oscillation changes caused by TBI and improve the prognosis of TBI.
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Affiliation(s)
- Xiaoyu Yang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Xuepei Li
- Medical Simulation Center, Chengdu First People's Hospital, Chengdu, China
| | - Yikai Yuan
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Tong Sun
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Jingguo Yang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Bo Deng
- College of Electronic Engineering (College of Meteorological Observation), Chengdu University of Information Technology, Chengdu, China
| | - Hang Yu
- College of Electronic Engineering (College of Meteorological Observation), Chengdu University of Information Technology, Chengdu, China
| | - Anliang Gao
- Department of Neurosurgery, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, China
| | - Junwen Guan
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Junwen Guan
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Abstract
In this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model's fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.
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Qiu T, Dai Q, Wang Q. A novel de novo hemizygous ARHGEF9 mutation associated with severe intellectual disability and epilepsy: a case report. J Int Med Res 2021; 49:3000605211058372. [PMID: 34851771 PMCID: PMC8647271 DOI: 10.1177/03000605211058372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
ARHGEF9 encodes collybistin, a brain-specific guanosine diphosphate-guanosine-5′-triphosphate exchange factor that plays an important role in clustering of gephyrin and γ-aminobutyric acid type A receptors in the postsynaptic membrane. Overwhelming evidence suggests that defects in this protein can cause X-linked intellectual disability, which comprises a series of clinical phenotypes, including autism spectrum disorder, behavior disorder, intellectual disability, and febrile seizures. Here, we report a boy with clinical symptoms of severe intellectual disability, epilepsy, and developmental delay and regression. Trio exome sequencing (trio-clinical exome sequencing) identified a novel hemizygous deletion, c.656_c.669delACTTCTTTGAGGCC (p. His219Leu fs*9), in exon 5 of ARHGEF9. This variant was not reported in either the Genome Aggregation Database or our database of 309 patients with neurodevelopmental disorders. Oxcarbazepine and levetiracetam reduced the frequency of the patient’s epileptic seizures to a certain extent, but psychomotor developmental delay and developmental regression became more obvious with age. This case study seeks to report a de novo loss-of-function mutation of ARHGEF9, aiming to emphasize the genetic diagnosis of X-linked intellectual disability and further improve knowledge of the ethnic distribution of ARHGEF9 mutations.
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Affiliation(s)
- Tong Qiu
- Division of Pediatrics, West China Second University Hospital, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, State Key Laboratory of Biotherapy and Collaborative Innovation Center of Biotherapy, Sichuan University, Chengdu, China
| | - Qian Dai
- Division of Pediatrics, West China Second University Hospital, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, State Key Laboratory of Biotherapy and Collaborative Innovation Center of Biotherapy, Sichuan University, Chengdu, China
| | - Qiu Wang
- Division of Rehabilitation Medicine, West China Second University Hospital, Sichuan University, Chengdu, China
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Beppi C, Ribeiro Violante I, Scott G, Sandrone S. EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions. Brain Cogn 2021; 148:105677. [PMID: 33486194 DOI: 10.1016/j.bandc.2020.105677] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 12/26/2020] [Accepted: 12/27/2020] [Indexed: 01/04/2023]
Abstract
Neural oscillations and their association with brain states and cognitive functions have been object of extensive investigation over the last decades. Several electroencephalography (EEG) and magnetoencephalography (MEG) analysis approaches have been explored and oscillatory properties have been identified, in parallel with the technical and computational advancement. This review provides an up-to-date account of how EEG/MEG oscillations have contributed to the understanding of cognition. Methodological challenges, recent developments and translational potential, along with future research avenues, are discussed.
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Affiliation(s)
- Carolina Beppi
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland; Department of Neurology, University Hospital Zurich and University of Zurich, Zurich, Switzerland; Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
| | - Inês Ribeiro Violante
- Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Brain Sciences, Imperial College London, London, United Kingdom; School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.
| | - Gregory Scott
- Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Brain Sciences, Imperial College London, London, United Kingdom.
| | - Stefano Sandrone
- Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Brain Sciences, Imperial College London, London, United Kingdom.
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Milosevic M, Marquez-Chin C, Masani K, Hirata M, Nomura T, Popovic MR, Nakazawa K. Why brain-controlled neuroprosthetics matter: mechanisms underlying electrical stimulation of muscles and nerves in rehabilitation. Biomed Eng Online 2020; 19:81. [PMID: 33148270 PMCID: PMC7641791 DOI: 10.1186/s12938-020-00824-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 10/10/2020] [Indexed: 12/11/2022] Open
Abstract
Delivering short trains of electric pulses to the muscles and nerves can elicit action potentials resulting in muscle contractions. When the stimulations are sequenced to generate functional movements, such as grasping or walking, the application is referred to as functional electrical stimulation (FES). Implications of the motor and sensory recruitment of muscles using FES go beyond simple contraction of muscles. Evidence suggests that FES can induce short- and long-term neurophysiological changes in the central nervous system by varying the stimulation parameters and delivery methods. By taking advantage of this, FES has been used to restore voluntary movement in individuals with neurological injuries with a technique called FES therapy (FEST). However, long-lasting cortical re-organization (neuroplasticity) depends on the ability to synchronize the descending (voluntary) commands and the successful execution of the intended task using a FES. Brain-computer interface (BCI) technologies offer a way to synchronize cortical commands and movements generated by FES, which can be advantageous for inducing neuroplasticity. Therefore, the aim of this review paper is to discuss the neurophysiological mechanisms of electrical stimulation of muscles and nerves and how BCI-controlled FES can be used in rehabilitation to improve motor function.
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Affiliation(s)
- Matija Milosevic
- Graduate School of Engineering Science, Department of Mechanical Science and Bioengineering, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan.
| | - Cesar Marquez-Chin
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, M5S 3G9, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, 520 Sutherland Drive, Toronto, ON, M4G 3V9, Canada
- CRANIA, University Health Network & University of Toronto, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
| | - Kei Masani
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, M5S 3G9, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, 520 Sutherland Drive, Toronto, ON, M4G 3V9, Canada
- CRANIA, University Health Network & University of Toronto, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
| | - Masayuki Hirata
- Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Taishin Nomura
- Graduate School of Engineering Science, Department of Mechanical Science and Bioengineering, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan
| | - Milos R Popovic
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, M5S 3G9, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, 520 Sutherland Drive, Toronto, ON, M4G 3V9, Canada
- CRANIA, University Health Network & University of Toronto, 550 University Avenue, Toronto, ON, M5G 2A2, Canada
| | - Kimitaka Nakazawa
- Department of Life Sciences, Graduate School of Arts and Sciences, University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo, 153-8902, Japan
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