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Kim MS, Park S, Park U, Kang SW, Kang SY. Fatigue in Parkinson's Disease Is Due to Decreased Efficiency of the Frontal Network: Quantitative EEG Analysis. J Mov Disord 2024; 17:304-312. [PMID: 38853446 PMCID: PMC11300402 DOI: 10.14802/jmd.24038] [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: 02/17/2024] [Accepted: 06/05/2024] [Indexed: 06/11/2024] Open
Abstract
OBJECTIVE Fatigue is a common, debilitating nonmotor symptom of Parkinson's disease (PD), but its mechanism is poorly understood. We aimed to determine whether electroencephalography (EEG) could objectively measure fatigue and to explore the pathophysiology of fatigue in PD. METHODS We studied 32 de novo PD patients who underwent EEG. We compared brain activity between 19 PD patients without fatigue and 13 PD patients with fatigue via EEG power spectra and graphs, including the global efficiency, characteristic path length, clustering coefficient, small-worldness, local efficiency, degree centrality, closeness centrality, and betweenness centrality. RESULTS No significant differences in absolute or relative power were detected between PD patients without or with fatigue (all p > 0.02, Bonferroni-corrected). According to our network analysis, brain network efficiency differed by frequency band. Generally, the brain network in the frontal area for theta and delta bands showed greater efficiency, and in the temporal area, the alpha1 band was less efficient in PD patients without fatigue (p < 0.0001, p = 0.0011, and p = 0.0007, respectively, Bonferroni-corrected). CONCLUSION Our study suggests that PD patients with fatigue have less efficient networks in the frontal area than PD patients without fatigue. These findings may explain why fatigue is common in PD, a frontostriatal disorder. Increased efficiency in the temporal area in PD patients with fatigue is assumed to be compensatory. Brain network analysis using graph theory is more valuable than power spectrum analysis in revealing the brain mechanism related to fatigue.
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Affiliation(s)
- Min Seung Kim
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Korea
| | | | | | - Seung Wan Kang
- iMediSync, Inc., Seoul, Korea
- National Standard Reference Data Center for Korean EEG, College of Nursing, Seoul National University, Seoul, Korea
| | - Suk Yun Kang
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Korea
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2
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Wilken S, Böttcher A, Adelhöfer N, Raab M, Beste C, Hoffmann S. Neural oscillations guiding action during effects imagery. Behav Brain Res 2024; 469:115063. [PMID: 38777262 DOI: 10.1016/j.bbr.2024.115063] [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: 12/19/2023] [Revised: 05/02/2024] [Accepted: 05/18/2024] [Indexed: 05/25/2024]
Abstract
Goal-directed acting requires the integration of sensory information but can also be performed without direct sensory input. Examples of this can be found in sports and can be conceptualized by feedforward processes. There is, however, still a lack of understanding of the temporal neural dynamics and neuroanatomical structures involved in such processes. In the current study, we used EEG beamforming methods and examined 37 healthy participants in two well-controlled experiments varying the necessity of anticipatory processes during goal-directed action. We found that alpha and beta activity in the medial and posterior cingulate cortex enabled feedforward predictions about the position of an object based on the latest sensorimotor state. On this basis, theta band activity seems more related to sensorimotor representations, while beta band activity would be more involved in setting up the structure of the neural representations themselves. Alpha band activity in sensory cortices reflects an intensified gating of the anticipated perceptual consequences of the to-be-executed action. Together, the findings indicate that goal-directed acting through the anticipation of the predicted state of an effector is based on accompanying processes in multiple frequency bands in midcingulate and sensory brain regions.
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Affiliation(s)
- Saskia Wilken
- General Psychology: Judgment, Decision Making, & Action, Institute of Psychology, University of Hagen, Hagen, Germany
| | - Adriana Böttcher
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany; University Neuropsychology Center, Faculty of Medicine, TU Dresden, Germany
| | - Nico Adelhöfer
- Donders Institute of Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Markus Raab
- Performance Psychology, Institute of Psychology, German Sport University Cologne, Cologne, Germany; School of Applied Sciences, London South Bank University, London, UK
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany; University Neuropsychology Center, Faculty of Medicine, TU Dresden, Germany; Shandong Normal University, Jinan, PR China
| | - Sven Hoffmann
- General Psychology: Judgment, Decision Making, & Action, Institute of Psychology, University of Hagen, Hagen, Germany.
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Șerban CA, Barborică A, Roceanu AM, Mîndruță IR, Ciurea J, Stancu M, Pâslaru AC, Zăgrean AM, Zăgrean L, Moldovan M. Towards an electroencephalographic measure of awareness based on the reactivity of oscillatory macrostates to hearing a subject's own name. Eur J Neurosci 2024; 59:771-785. [PMID: 37675619 DOI: 10.1111/ejn.16138] [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: 10/12/2022] [Revised: 08/09/2023] [Accepted: 08/16/2023] [Indexed: 09/08/2023]
Abstract
We proposed that the brain's electrical activity is composed of a sequence of alternating states with repeating topographic spectral distributions on scalp electroencephalogram (EEG), referred to as oscillatory macrostates. The macrostate showing the largest decrease in the probability of occurrence, measured as a percentage (reactivity), during sensory stimulation was labelled as the default EEG macrostate (DEM). This study aimed to assess the influence of awareness on DEM reactivity (DER). We included 11 middle cerebral artery ischaemic stroke patients with impaired awareness having a median Glasgow Coma Scale (GCS) of 6/15 and a group of 11 matched healthy controls. EEG recordings were carried out during auditory 1 min stimulation epochs repeating either the subject's own name (SON) or the SON in reverse (rSON). The DEM was identified across three SON epochs alternating with three rSON epochs. Compared with the patients, the DEM of controls contained more posterior theta activity reflecting source dipoles that could be mapped in the posterior cingulate cortex. The DER was measured from the 1 min quiet baseline preceding each stimulation epoch. The difference in mean DER between the SON and rSON epochs was measured by the salient EEG reactivity (SER) theoretically ranging from -100% to 100%. The SER was 12.4 ± 2.7% (Mean ± standard error of the mean) in controls and only 1.3 ± 1.9% in the patient group (P < 0.01). The patient SER decreased with the Glasgow Coma Scale. Our data suggest that awareness increases DER to SON as measured by SER.
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Affiliation(s)
- Cosmin-Andrei Șerban
- Physics Department, University of Bucharest, Bucharest, Romania
- Termobit Prod SRL, Bucharest, Romania
- FHC Inc, Bowdoin, Maine, USA
| | - Andrei Barborică
- Physics Department, University of Bucharest, Bucharest, Romania
- Termobit Prod SRL, Bucharest, Romania
- FHC Inc, Bowdoin, Maine, USA
| | | | | | - Jan Ciurea
- Department of Neurosurgery, Bagdasar-Arseni Emergency Hospital, Bucharest, Romania
| | - Mihai Stancu
- Division of Physiology and Neuroscience, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Division of Neurobiology, Faculty of Biology, Ludwig Maximilian University, Munich, Germany
| | - Alexandru C Pâslaru
- Division of Physiology and Neuroscience, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Ana-Maria Zăgrean
- Division of Physiology and Neuroscience, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Leon Zăgrean
- Division of Physiology and Neuroscience, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Mihai Moldovan
- Termobit Prod SRL, Bucharest, Romania
- Division of Physiology and Neuroscience, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
- Clinical Neurophysiology and Neurology, Rigshospitalet, Copenhagen, Denmark
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4
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Wisniewski MG, Joyner CN, Zakrzewski AC, Makeig S. Finding tau rhythms in EEG: An independent component analysis approach. Hum Brain Mapp 2024; 45:e26572. [PMID: 38339905 PMCID: PMC10823759 DOI: 10.1002/hbm.26572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 12/05/2023] [Accepted: 12/10/2023] [Indexed: 02/12/2024] Open
Abstract
Tau rhythms are largely defined by sound responsive alpha band (~8-13 Hz) oscillations generated largely within auditory areas of the superior temporal gyri. Studies of tau have mostly employed magnetoencephalography or intracranial recording because of tau's elusiveness in the electroencephalogram. Here, we demonstrate that independent component analysis (ICA) decomposition can be an effective way to identify tau sources and study tau source activities in EEG recordings. Subjects (N = 18) were passively exposed to complex acoustic stimuli while the EEG was recorded from 68 electrodes across the scalp. Subjects' data were split into 60 parallel processing pipelines entailing use of five levels of high-pass filtering (passbands of 0.1, 0.5, 1, 2, and 4 Hz), three levels of low-pass filtering (25, 50, and 100 Hz), and four different ICA algorithms (fastICA, infomax, adaptive mixture ICA [AMICA], and multi-model AMICA [mAMICA]). Tau-related independent component (IC) processes were identified from this data as being localized near the superior temporal gyri with a spectral peak in the 8-13 Hz alpha band. These "tau ICs" showed alpha suppression during sound presentations that was not seen for other commonly observed IC clusters with spectral peaks in the alpha range (e.g., those associated with somatomotor mu, and parietal or occipital alpha). The choice of analysis parameters impacted the likelihood of obtaining tau ICs from an ICA decomposition. Lower cutoff frequencies for high-pass filtering resulted in significantly fewer subjects showing a tau IC than more aggressive high-pass filtering. Decomposition using the fastICA algorithm performed the poorest in this regard, while mAMICA performed best. The best combination of filters and ICA model choice was able to identify at least one tau IC in the data of ~94% of the sample. Altogether, the data reveal close similarities between tau EEG IC dynamics and tau dynamics observed in MEG and intracranial data. Use of relatively aggressive high-pass filters and mAMICA decomposition should allow researchers to identify and characterize tau rhythms in a majority of their subjects. We believe adopting the ICA decomposition approach to EEG analysis can increase the rate and range of discoveries related to auditory responsive tau rhythms.
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Affiliation(s)
| | | | | | - Scott Makeig
- Swartz Center for Computational NeuroscienceUniversity of California San DiegoLa JollaCaliforniaUSA
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5
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Hubbard RJ, Federmeier KD. The Impact of Linguistic Prediction Violations on Downstream Recognition Memory and Sentence Recall. J Cogn Neurosci 2024; 36:1-23. [PMID: 37902591 PMCID: PMC10864033 DOI: 10.1162/jocn_a_02078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
Predicting upcoming words during language comprehension not only affects processing in the moment but also has consequences for memory, although the source of these memory effects (e.g., whether driven by lingering pre-activations, re-analysis following prediction violations, or other mechanisms) remains underspecified. Here, we investigated downstream impacts of prediction on memory in two experiments. First, we recorded EEG as participants read strongly and weakly constraining sentences with expected, unexpected but plausible, or semantically anomalous endings ("He made a holster for his gun / father / train") and were tested on their recognition memory for the sentence endings. Participants showed similar rates of false alarms for predicted but never presented sentence endings whether the prediction violation was plausible or anomalous, suggesting that these arise from pre-activation of the expected words during reading. During sentence reading, especially in strongly constraining sentences, plausible prediction violations elicited an anterior positivity; anomalous endings instead elicited a posterior positivity, whose amplitude was predictive of later memory for those anomalous words. ERP patterns at the time of recognition differentiated plausible and anomalous sentence endings: Words that had been plausible prediction violations elicited enhanced late positive complex amplitudes, suggesting greater episodic recollection, whereas anomalous sentence endings elicited greater N1 amplitudes, suggesting attentional tagging. In a follow-up behavioral study, a separate group of participants read the same sentence stimuli and were tested for sentence-level recall. We found that recall of full sentences was impaired when sentences ended with a prediction violation. Taken together, the results suggest that prediction violations draw attention and affect encoding of the violating word, in a manner that depends on plausibility, and that this, in turn, may impair future memory of the gist of the sentence.
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6
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Ye S, Bagić A, He B. Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy. Brain Topogr 2024; 37:152-168. [PMID: 38112884 PMCID: PMC10771380 DOI: 10.1007/s10548-023-01025-z] [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: 05/15/2023] [Accepted: 11/20/2023] [Indexed: 12/21/2023]
Abstract
The objective of this study is to extract pathological brain networks from interictal period of E/MEG recordings to localize epileptic foci for presurgical evaluation. We proposed here a resting state E/MEG analysis framework, to disentangle brain functional networks represented by neural oscillations. By using an Embedded Hidden Markov Model, we constructed a state space for resting state recordings consisting of brain states with different spatiotemporal patterns. Functional connectivity analysis along with graph theory was applied on the extracted brain states to quantify the network features of the extracted brain states, based on which the source location of pathological states is determined. The method is evaluated by computer simulations and our simulation results revealed the proposed framework can extract brain states with high accuracy regarding both spatial and temporal profiles. We further evaluated the framework as compared with intracranial EEG defined seizure onset zone in 10 patients with drug-resistant focal epilepsy who underwent MEG recordings and were seizure free after surgical resection. The real patient data analysis showed very good localization results using the extracted pathological brain states in 6/10 patients, with localization error of about 15 mm as compared to the seizure onset zone. We show that the pathological brain networks can be disentangled from the resting-state electromagnetic recording and could be identified based on the connectivity features. The framework can serve as a useful tool in extracting brain functional networks from noninvasive resting state electromagnetic recordings, and promises to offer an alternative to aid presurgical evaluation guiding intracranial EEG electrodes implantation.
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Affiliation(s)
- Shuai Ye
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA
| | - Anto Bagić
- Department of Neurology, University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), University of Pittsburgh Medical School, Pittsburgh, PA, USA
| | - Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA.
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7
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Huang J, Zhang G, Dang J, Chen Y, Miyamoto S. Semantic processing during continuous speech production: an analysis from eye movements and EEG. Front Hum Neurosci 2023; 17:1253211. [PMID: 37727862 PMCID: PMC10505728 DOI: 10.3389/fnhum.2023.1253211] [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: 07/05/2023] [Accepted: 08/22/2023] [Indexed: 09/21/2023] Open
Abstract
Introduction Speech production involves neurological planning and articulatory execution. How speakers prepare for articulation is a significant aspect of speech production research. Previous studies have focused on isolated words or short phrases to explore speech planning mechanisms linked to articulatory behaviors, including investigating the eye-voice span (EVS) during text reading. However, these experimental paradigms lack real-world speech process replication. Additionally, our understanding of the neurological dimension of speech planning remains limited. Methods This study examines speech planning mechanisms during continuous speech production by analyzing behavioral (eye movement and speech) and neurophysiological (EEG) data within a continuous speech production task. The study specifically investigates the influence of semantic consistency on speech planning and the occurrence of "look ahead" behavior. Results The outcomes reveal the pivotal role of semantic coherence in facilitating fluent speech production. Speakers access lexical representations and phonological information before initiating speech, emphasizing the significance of semantic processing in speech planning. Behaviorally, the EVS decreases progressively during continuous reading of regular sentences, with a slight increase for non-regular sentences. Moreover, eye movement pattern analysis identifies two distinct speech production modes, highlighting the importance of semantic comprehension and prediction in higher-level lexical processing. Neurologically, the dual pathway model of speech production is supported, indicating a dorsal information flow and frontal lobe involvement. The brain network linked to semantic understanding exhibits a negative correlation with semantic coherence, with significant activation during semantic incoherence and suppression in regular sentences. Discussion The study's findings enhance comprehension of speech planning mechanisms and offer insights into the role of semantic coherence in continuous speech production. Furthermore, the research methodology establishes a valuable framework for future investigations in this domain.
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Affiliation(s)
- Jinfeng Huang
- Faculty of Human Sciences, University of Tsukuba, Ibaraki, Japan
- Research Institute, NeuralEcho Technology Co., Ltd., Beijing, China
| | - Gaoyan Zhang
- Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jianwu Dang
- Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Yu Chen
- Technical College for the Deaf, Tianjin University of Technology, Tianjin, China
| | - Shoko Miyamoto
- Faculty of Human Sciences, University of Tsukuba, Ibaraki, Japan
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8
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Du Y, Kong Y, He X. IABC: A Toolbox for Intelligent Analysis of Brain Connectivity. Neuroinformatics 2023; 21:303-321. [PMID: 36609668 DOI: 10.1007/s12021-022-09617-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/30/2022] [Indexed: 01/09/2023]
Abstract
Brain functional networks and connectivity have played an important role in exploring brain function for understanding the brain and disclosing the mechanisms of brain disorders. Independent component analysis (ICA) is one of the most widely applied data-driven methods to extract brain functional networks/connectivity. However, it is hard to guarantee the reliability of networks/connectivity due to the randomness of component order and the difficulty in selecting an optimal component number in ICA. To facilitate the analysis of brain functional networks and connectivity using ICA, we developed a MATLAB toolbox called Intelligent Analysis of Brain Connectivity (IABC). IABC incorporates our previously proposed group information guided independent component analysis (GIG-ICA), NeuroMark, and splitting-merging assisted reliable ICA (SMART ICA) methods, which can estimate reliable individual-subject neuroimaging measures for further analysis. After user inputs functional magnetic resonance imaging (fMRI) data of multiple subjects that are regularly organized (e.g., in Brain Imaging Data Structure (BIDS)) and clicks a few buttons to set parameters, IABC automatically outputs brain functional networks, their related time courses, and functional network connectivity of each subject. All these neuroimaging measures are promising for providing clues in understanding brain function and differentiating brain disorders.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China.
| | - Yanshu Kong
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Xingyu He
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
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9
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Ismail L, Karwowski W, Farahani FV, Rahman M, Alhujailli A, Fernandez-Sumano R, Hancock PA. Modeling Brain Functional Connectivity Patterns during an Isometric Arm Force Exertion Task at Different Levels of Perceived Exertion: A Graph Theoretical Approach. Brain Sci 2022; 12:1575. [PMID: 36421899 PMCID: PMC9688629 DOI: 10.3390/brainsci12111575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/09/2022] [Accepted: 11/13/2022] [Indexed: 09/29/2023] Open
Abstract
The perception of physical exertion is the cognitive sensation of work demands associated with voluntary muscular actions. Measurements of exerted force are crucial for avoiding the risk of overexertion and understanding human physical capability. For this purpose, various physiological measures have been used; however, the state-of-the-art in-force exertion evaluation lacks assessments of underlying neurophysiological signals. The current study applied a graph theoretical approach to investigate the topological changes in the functional brain network induced by predefined force exertion levels for twelve female participants during an isometric arm task and rated their perceived physical comfort levels. The functional connectivity under predefined force exertion levels was assessed using the coherence method for 84 anatomical brain regions of interest at the electroencephalogram (EEG) source level. Then, graph measures were calculated to quantify the network topology for two frequency bands. The results showed that high-level force exertions are associated with brain networks characterized by more significant clustering coefficients (6%), greater modularity (5%), higher global efficiency (9%), and less distance synchronization (25%) under alpha coherence. This study on the neurophysiological basis of physical exertions with various force levels suggests that brain regions communicate and cooperate higher when muscle force exertions increase to meet the demands of physically challenging tasks.
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Affiliation(s)
- Lina Ismail
- Department of Industrial and Management Engineering, Arab Academy for Science Technology & Maritime Transport, Alexandria 2913, Egypt
| | - Waldemar Karwowski
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
| | - Farzad V. Farahani
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Mahjabeen Rahman
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
| | - Ashraf Alhujailli
- Department of Management Science, Yanbu Industrial College, Yanbu 46452, Saudi Arabia
| | - Raul Fernandez-Sumano
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
| | - P. A. Hancock
- Department of Psychology, University of Central Florida, Orlando, FL 32816, USA
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10
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Neural Research on Depth Perception and Stereoscopic Visual Fatigue in Virtual Reality. Brain Sci 2022; 12:brainsci12091231. [PMID: 36138967 PMCID: PMC9497221 DOI: 10.3390/brainsci12091231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/04/2022] [Accepted: 09/07/2022] [Indexed: 11/29/2022] Open
Abstract
Virtual reality (VR) technology provides highly immersive depth perception experiences; nevertheless, stereoscopic visual fatigue (SVF) has become an important factor currently hindering the development of VR applications. However, there is scant research on the underlying neural mechanism of SVF, especially those induced by VR displays, which need further research. In this paper, a Go/NoGo paradigm based on disparity variations is proposed to induce SVF associated with depth perception, and the underlying neural mechanism of SVF in a VR environment was investigated. The effects of disparity variations as well as SVF on the temporal characteristics of visual evoked potentials (VEPs) were explored. Point-by-point permutation statistical with repeated measures ANOVA results revealed that the amplitudes and latencies of the posterior VEP component P2 were modulated by disparities, and posterior P2 amplitudes were modulated differently by SVF in different depth perception situations. Cortical source localization analysis was performed to explore the original cortex areas related to certain fatigue levels and disparities, and the results showed that posterior P2 generated from the precuneus could represent depth perception in binocular vision, and therefore could be performed to distinguish SVF induced by disparity variations. Our findings could help to extend an understanding of the neural mechanisms underlying depth perception and SVF as well as providing beneficial information for improving the visual experience in VR applications.
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11
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Wang TC, Huang YY, Duann JR. Sources of independent mu components reveal different brain areas involved in motor imagery, motor execution, and movement observation. Brain Res 2022; 1796:148075. [PMID: 36084693 DOI: 10.1016/j.brainres.2022.148075] [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: 05/14/2022] [Revised: 08/30/2022] [Accepted: 09/02/2022] [Indexed: 11/02/2022]
Abstract
To answer the question of whether the same brain circuit(s) facilitates motor imagery (MI), motor execution (ME), and movement observation (MO), we conducted electroencephalography (EEG) experiment combining the three motor conditions in the same experimental runs. The EEG data were analyzed using two different independent component analysis (ICA) decomposition approaches: a single ICA decomposition on all EEG data combined and separate ICA decomposition on the EEG data obtained from the separate conditions. The results indicated that the separate ICA approach may provide a better fit to the EEG data obtained from the separate conditions to deliver specific independent right mu components with distinct topographies for each of the motor conditions. The topography of the MI condition covered the brain regions posterior to the central sulcus (P4 EEG channel); the ME condition covered the brain regions anterior to the central sulcus (C4 EEG channel), and the MO condition had broader coverage with the main activation in the premotor region (CP4 EEG channel). The source localization results also exhibited significant differences among the motor conditions. In addition, the result of single ICA decomposition resembled the result of separate ICA decomposition on the EEG data of ME with similar topographies and closely located EEG sources. This finding may further indicate that the result of single ICA decomposition may be dominated by the ME motor condition because it manifests higher data variance than the other two motor conditions.
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Affiliation(s)
- Tien-Ching Wang
- Institute of Cognitive Neuroscience, National Central University, Taoyuan 32010, Taiwan
| | - Yu-Yu Huang
- Institute of Cognitive Neuroscience, National Central University, Taoyuan 32010, Taiwan
| | - Jeng-Ren Duann
- Institute of Education, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan; Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, United States.
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12
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Liao G, Wang S, Wei Z, Liu B, Okubo R, Hernandez ME. Online classifier of AMICA model to evaluate state anxiety while standing in virtual reality. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:381-384. [PMID: 36086599 DOI: 10.1109/embc48229.2022.9871843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Changes in emotional state, such as anxiety, have a significant impact on behavior and mental health. However, the detection of anxiety in individuals requires trained specialists to administer specialized assessments, which often take a significant amount of time and resources. Thus, there is a significant need for objective and real-time anxiety detection methods to aid clinical practice. Recent advances in Adaptive Mixture Independent Component Analysis (AMICA) have demonstrated the ability to detect changes in emotional states using electroencephalographic (EEG) data. However, given that several hours may be need to identify the different models, alternative methods must be sought for future brain-computer-interface applications. This study examines the feasibility of a machine learning classifier using frequency domain features of EEG data to classify individual 500 ms samples of EEG data into different cortical states, as established by multi-model AMICA labels. Using a random forest classifier with 12 input features from EEG data to predict cortical states yielded a 75% accuracy in binary classification. Based on these findings, this work may provide a foundation for real-time anxiety state detection and classification.
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Jung KH, Kang DJ, Lee WJ, Son HS, Kim S, Kang SW. Pathophysiological insight into transient global amnesia from quantitative electroencephalography. Neurobiol Dis 2022; 170:105778. [PMID: 35636647 DOI: 10.1016/j.nbd.2022.105778] [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: 03/16/2022] [Revised: 05/04/2022] [Accepted: 05/23/2022] [Indexed: 11/29/2022] Open
Abstract
Transient global amnesia (TGA) is recognized as a benign memory disorder, with characteristic clinical and imaging features. However, the pathophysiology of TGA remains elusive. This study aims to elucidate the pathophysiological changes underlying TGA by exploring the brain activities. In total, 215 patients with TGA (age: 61.8 ± 7.8 years; women: 146) with MRI (within 7 days) and EEG studies (within 90 days) were recruited. Quantitative EEG (QEEG) power spectra and network analysis were performed by the artificial intelligence EEG analysis platform (iSyncBrain®). Subgroup analyses were conducted for different clinical groups, based on symptom duration, EEG timing after onset, and cytotoxic lesions on the MRI. Compared with 252 age- and sex-matched subjects (age: 64.5 ± 8.3 years, women: 182), TGA patients showed a global decrease in absolute power in all band waves, a relative decrease in alpha waves, a relative increase in theta waves, and atypical compensation activity. These QEEG changes were observed regardless of having cytotoxic lesions in MRI and they were significant up to 1 week after symptom onset. Network analysis showed that TGA was more activated than normal controls in alpha1 band-waves, exhibiting a compensatory process. TGA results in prolonged and widespread alterations of brain activity and connectivity. QEEG provide insight into pathophysiology of TGA.
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Affiliation(s)
- Keun-Hwa Jung
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea; Program in Neuroscience, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | | | - Woo-Jin Lee
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Hyo-Shin Son
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sohyun Kim
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Seung Wan Kang
- iMediSync Inc., Seoul, Republic of Korea; National Standard Reference Data Center for Korean EEG, Seoul National University College of Nursing, Republic of Korea.
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14
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Kipiński L, Maciejowski A, Małyszczak K, Pilecki W. High-frequency changes in single-trial visual evoked potentials for unattended stimuli in chronic schizophrenia. J Neurosci Methods 2022; 377:109626. [DOI: 10.1016/j.jneumeth.2022.109626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 04/26/2022] [Accepted: 05/18/2022] [Indexed: 10/18/2022]
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15
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Yue K, Guo M, Liu Y, Hu H, Lu K, Chen S, Wang D. Investigate the Neuro Mechanisms of Stereoscopic Visual Fatigue. IEEE J Biomed Health Inform 2022; 26:2963-2973. [PMID: 35316199 DOI: 10.1109/jbhi.2022.3161083] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Stereoscopic visual fatigue (SVF) due to prolonged immersion in the virtual environment can lead to negative user experience, thus hindering the development of virtual reality (VR) industry. Previous studies have focused on investigating the evaluation indicators associated with SVF, while few studies have been conducted to reveal the underlying neural mechanism, especially in VR applications. In this paper, a modified Go/NoGo paradigm was adopted to induce SVF in VR environment with Go trials for maintaining participants' attention to experimental viewing tasks and NoGo trials for investigating the neural effects under SVF. Random dot stereograms (RDSs) with 11 disparities and 2 types of shapes (arrow and rectangle) were presented to evoke the depth-related visual evoked potentials (DVEPs) during 64-channel EEG recordings. EEG datasets collected from 15 participants in NoGo trials were selected to conduct individual processing and group analysis, in which the characteristics of the DVEPs components for various fatigue degrees were compared with one-way repeated-measurement ANOVA and independent components were clustered to explore the original cortex areas related to SVF. Point-by-point permutation statistics revealed that DVEPs sample points from 230ms to 280ms in most brain areas changed significantly with SVF. More specifically, we found that amplitudes of component P2 changed significantly when SVF increased. Additionally, independent component analysis (ICA) identified that component P2 which originated from posterior cingulate cortex and precuneus, was associated statistically with SVF. We believe that SVF is rather a conscious status concerning the changes of self-awareness or self-location awareness than the performance reduction of retinal image processing. Moreover, we suggest that indicators representing higher conscious state may be a better indicator for SVF evaluation in VR environments.
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16
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Which BSS method separates better the EEG Signals? A comparison of five different algorithms. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103292] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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17
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Hsu SH, Lin Y, Onton J, Jung TP, Makeig S. Unsupervised Learning of Brain State Dynamics during Emotion Imagination using High-Density EEG. Neuroimage 2022; 249:118873. [PMID: 34998969 DOI: 10.1016/j.neuroimage.2022.118873] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 11/08/2021] [Accepted: 01/04/2022] [Indexed: 11/28/2022] Open
Abstract
This study applies adaptive mixture independent component analysis (AMICA) to learn a set of ICA models, each optimized by fitting a distributional model for each identified component process while maximizing component process independence within some subsets of time points of a multi-channel EEG dataset. Here, we applied 20-model AMICA decomposition to long-duration (1-2 hr), high-density (128-channel) EEG data recorded while participants used guided imagination to imagine situations stimulating the experience of 15 specified emotions. These decompositions tended to return models identifying spatiotemporal EEG patterns or states within single emotion imagination periods. Model probability transitions reflected time-courses of EEG dynamics during emotion imagination, which varied across emotions. Transitions between models accounting for imagined "grief" and "happiness" were more abrupt and better aligned with participant reports, while transitions for imagined "contentment" extended into adjoining "relaxation" periods. The spatial distributions of brain-localizable independent component processes (ICs) were more similar within participants (across emotions) than emotions (across participants). Across participants, brain regions with differences in IC spatial distributions (i.e., dipole density) between emotion imagination versus relaxation were identified in or near the left rostrolateral prefrontal, posterior cingulate cortex, right insula, bilateral sensorimotor, premotor, and associative visual cortex. No difference in dipole density was found between positive versus negative emotions. AMICA models of changes in high-density EEG dynamics may allow data-driven insights into brain dynamics during emotional experience, possibly enabling the improved performance of EEG-based emotion decoding and advancing our understanding of emotion.
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Affiliation(s)
- Sheng-Hsiou Hsu
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA.
| | - Yayu Lin
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA
| | - Julie Onton
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA
| | - Tzyy-Ping Jung
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA
| | - Scott Makeig
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA
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18
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Ji Y, Choi TY, Lee J, Yoon S, Won GH, Jeong H, Kang SW, Kim JW. Characteristics of Attention-Deficit/Hyperactivity Disorder Subtypes in Children Classified Using Quantitative Electroencephalography. Neuropsychiatr Dis Treat 2022; 18:2725-2736. [PMID: 36437880 PMCID: PMC9697401 DOI: 10.2147/ndt.s386774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 11/11/2022] [Indexed: 11/22/2022] Open
Abstract
PURPOSE This study used quantitative electroencephalography (QEEG) to investigate the characteristics of attention-deficit/hyperactivity disorder (ADHD) subtypes in children. PATIENTS AND METHODS There were 69 subjects (42 with ADHD and 27 neurotypical (NT)) in this study. A semi-structured interview was conducted with each participant for psychiatric diagnostic evaluation. We measured the absolute and relative power in 19 channels and analyzed QEEG using the following frequency ranges: delta (1-4 Hz), theta (4-8 Hz), alpha 1 (8-10 Hz), alpha 2 (10-12 Hz), beta 1 (12-15 Hz), beta 2 (15-20 Hz), beta 3 (20-30 Hz), and gamma (30-45 Hz). Group analyses and EEG noise preprocessing were conducted using iSyncBrain, a cloud-based, artificial intelligence EEG analysis platform. Analysis of covariance adjusted for IQ, age, and sex was used. RESULTS QEEG analysis revealed three ADHD subtypes, characterized by (A) elevated relative fast alpha and beta power, (B) elevated absolute slow frequency (delta and theta power), or (C) elevated absolute and relative beta power. A significant difference was found in the Korean ADHD Rating Scale (K-ARS) among the four groups (df=3, F=8.004, p<0.001); group C had the highest score (25.31±11.16), followed by group A (21.67±13.18). The score of group B (12.64±7.84) was similar to that of the NT group (11.07±6.12) and did not reach the cut-off point of the K-ARS. In the Wender-Utah Rating Scale (WURS), group B score (55.82±23.17) was significantly higher than the NT group score (42.81±13.26). CONCLUSION These results indicate that children with ADHD do not constitute a neurophysiologically homogenous group. Children with QEEG subtype B (elevated slow frequency) may be difficult to distinguish from normal children using the K-ARS, which is the most common screening tool for ADHD. Moreover, parents of children with this subtype may be less sensitive to observing ADHD symptoms.
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Affiliation(s)
- Yoonmi Ji
- Department of Psychiatry, Daegu Catholic University School of Medicine, Daegu, Republic of Korea
| | - Tae Young Choi
- Department of Psychiatry, Daegu Catholic University School of Medicine, Daegu, Republic of Korea
| | - Jonghun Lee
- Department of Psychiatry, Daegu Catholic University School of Medicine, Daegu, Republic of Korea
| | - Seoyoung Yoon
- Department of Psychiatry, Daegu Catholic University School of Medicine, Daegu, Republic of Korea
| | - Geun Hui Won
- Department of Psychiatry, Daegu Catholic University School of Medicine, Daegu, Republic of Korea
| | | | - Seung Wan Kang
- iMediSync Inc, Seoul, Republic of Korea.,National Standard Reference Data Center for Korean EEG, Seoul National University College of Nursing, Seoul, Republic of Korea
| | - Jun Won Kim
- Department of Psychiatry, Daegu Catholic University School of Medicine, Daegu, Republic of Korea
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19
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Serban CA, Barborica A, Roceanu AM, Mindruta I, Ciurea J, Pâslaru AC, Zăgrean AM, Zăgrean L, Moldovan M. A method to assess the default EEG macrostate and its reactivity to stimulation. Clin Neurophysiol 2021; 134:50-64. [PMID: 34973517 DOI: 10.1016/j.clinph.2021.12.002] [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: 02/07/2021] [Revised: 08/23/2021] [Accepted: 12/04/2021] [Indexed: 11/16/2022]
Abstract
OBJECTIVE The default mode network (DMN) is deactivated by stimulation. We aimed to assess the DMN reactivity impairment by routine EEG recordings in stroke patients with impaired consciousness. METHODS Binocular light flashes were delivered at 1 Hz in 1-minute epochs, following a 1-minute baseline (PRE). The EEG was decomposed in a series of binary oscillatory macrostates by topographic spectral clustering. The most deactivated macrostate was labeled the default EEG macrostate (DEM). Its reactivity (DER) was quantified as the decrease in DEM occurrence probability during stimulation. A normalized DER index (DERI) was calculated as DER/PRE. The measures were compared between 14 healthy controls and 32 comatose patients under EEG monitoring following an acute stroke. RESULTS The DEM was mapped to the posterior DMN hubs. In the patients, these DEM source dipoles were 3-4 times less frequent and were associated with an increased theta activity. Even in a reduced 6-channel montage, a DER below 6.26% corresponding to a DERI below 0.25 could discriminate the patients with sensitivity and specificity well above 80%. CONCLUSION The method detected the DMN impairment in post-stroke coma patients. SIGNIFICANCE The DEM and its reactivity to stimulation could be useful to monitor the DMN function at bedside.
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Affiliation(s)
- Cosmin-Andrei Serban
- Physics Department, University of Bucharest, Romania; Termobit Prod SRL, Bucharest, Romania; FHC Inc, Bowdoin, ME, USA.
| | - Andrei Barborica
- Physics Department, University of Bucharest, Romania; Termobit Prod SRL, Bucharest, Romania; FHC Inc, Bowdoin, ME, USA.
| | | | - Ioana Mindruta
- Neurology Department, University Emergency Hospital, Bucharest, Romania.
| | - Jan Ciurea
- Department of Neurosurgery, Bagdasar-Arseni Emergency Hospital, Bucharest, Romania.
| | - Alexandru C Pâslaru
- Division of Physiology and Neuroscience, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Ana-Maria Zăgrean
- Division of Physiology and Neuroscience, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Leon Zăgrean
- Division of Physiology and Neuroscience, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Mihai Moldovan
- Termobit Prod SRL, Bucharest, Romania; Division of Physiology and Neuroscience, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; Neuroscience, University of Copenhagen, Copenhagen, Denmark; Department of Clinical Neurophysiology, Rigshospitalet, Copenhagen, Denmark.
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20
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Song S, Nordin AD. Mobile Electroencephalography for Studying Neural Control of Human Locomotion. Front Hum Neurosci 2021; 15:749017. [PMID: 34858154 PMCID: PMC8631362 DOI: 10.3389/fnhum.2021.749017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 10/05/2021] [Indexed: 01/09/2023] Open
Abstract
Walking or running in real-world environments requires dynamic multisensory processing within the brain. Studying supraspinal neural pathways during human locomotion provides opportunities to better understand complex neural circuity that may become compromised due to aging, neurological disorder, or disease. Knowledge gained from studies examining human electrical brain dynamics during gait can also lay foundations for developing locomotor neurotechnologies for rehabilitation or human performance. Technical barriers have largely prohibited neuroimaging during gait, but the portability and precise temporal resolution of non-invasive electroencephalography (EEG) have expanded human neuromotor research into increasingly dynamic tasks. In this narrative mini-review, we provide a (1) brief introduction and overview of modern neuroimaging technologies and then identify considerations for (2) mobile EEG hardware, (3) and data processing, (4) including technical challenges and possible solutions. Finally, we summarize (5) knowledge gained from human locomotor control studies that have used mobile EEG, and (6) discuss future directions for real-world neuroimaging research.
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Affiliation(s)
- Seongmi Song
- Department of Health and Kinesiology, Texas A&M University, College Station, TX, United States
| | - Andrew D Nordin
- Department of Health and Kinesiology, Texas A&M University, College Station, TX, United States
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
- Texas A&M Institute for Neuroscience, College Station, TX, United States
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21
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Si X, Han S, Zhang K, Zhang L, Sun Y, Yu J, Ming D. The Temporal Dynamics of EEG Microstate Reveals the Neuromodulation Effect of Acupuncture With Deqi. Front Neurosci 2021; 15:715512. [PMID: 34720853 PMCID: PMC8549605 DOI: 10.3389/fnins.2021.715512] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/10/2021] [Indexed: 02/01/2023] Open
Abstract
The electroencephalography (EEG) microstate has recently emerged as a new whole-brain mapping tool for studying the temporal dynamics of the human brain. Meanwhile, the neuromodulation effect of external stimulation on the human brain is of increasing interest to neuroscientists. Acupuncture, which originated in ancient China, is recognized as an external neuromodulation method with therapeutic effects. Effective acupuncture could elicit the deqi effect, which is a combination of multiple sensations. However, whether the EEG microstate could be used to reveal the neuromodulation effect of acupuncture with deqi remains largely unclear. In this study, multichannel EEG data were recorded from 16 healthy subjects during acupuncture manipulation, as well as during pre- and post-manipulation tactile controls and pre- and post-acupuncture rest controls. As the basic acupuncture unit for regulating the central nervous system, the Hegu acupoint was used in this study, and each subject’s acupuncture deqi behavior scores were collected. To reveal the neuroimaging evidence of acupuncture with deqi, EEG microstate analysis was conducted to obtain the microstate maps and microstate parameters for different conditions. Furthermore, Pearson’s correlation was analyzed to investigate the correlation relationship between microstate parameters and deqi behavioral scores. Results showed that: (1) compared with tactile controls, acupuncture manipulation caused significantly increased deqi behavioral scores. (2) Acupuncture manipulation significantly increased the duration, occurrence, and contribution parameters of microstate C, whereas it decreased those parameters of microstate D. (3) Microstate C’s duration parameter showed a significantly positive correlation with acupuncture deqi behavior scores. (4) Acupuncture manipulation significantly increased the transition probabilities with microstate C as node, whereas it reduced the transition probabilities with microstate D as node. (5) Microstate B→C’s transition probability also showed a significantly positive correlation with acupuncture deqi behavior scores. Taken together, the temporal dynamic feature of EEG microstate could be used as objective neuroimaging evidence to reveal the neuromodulation effect of acupuncture with deqi.
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Affiliation(s)
- Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China.,Tianjin International Engineering Institute, Tianjin University, Tianjin, China.,Institute of Applied Psychology, Tianjin University, Tianjin, China
| | - Shunli Han
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Kuo Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Ludan Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Yulin Sun
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Jiayue Yu
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China.,Tianjin International Engineering Institute, Tianjin University, Tianjin, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
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22
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Kipiński L, Kordecki W. Time-series analysis of trial-to-trial variability of MEG power spectrum during rest state, unattended listening, and frequency-modulated tones classification. J Neurosci Methods 2021; 363:109318. [PMID: 34400211 DOI: 10.1016/j.jneumeth.2021.109318] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 08/07/2021] [Accepted: 08/09/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND The nonstationarity of EEG/MEG signals is important for understanding the functioning of the human brain. From our previous research we know that short, 250-500-ms MEG signals are variance-nonstationary. The covariance of a stochastic process is mathematically associated with its spectral density, therefore we investigate how the spectrum of such nonstationary signals varies in time. NEW METHOD We analyse data from 148-channel MEG, which represent rest state, unattended listening, and frequency-modulated tones classification. We transform short-time MEG signals to the frequency domain and for the dominant frequencies of 8-12 Hz we prepare the time series representing their trial-to-trial variability. Then, we test them for level- and trend-stationarity, unit root, heteroscedasticity, and gaussianity, and propose ARMA-modelling for their description. RESULTS The analysed time series have weak-stationarity properties independently of the functional state of the brain and channel localization. Only a small percentage of them, mostly related to the cognitive task, reveal nonstationarity. The obtained mathematical models show that the spectral density of the analysed signals depends on only two to three previous trials. COMPARISON WITH EXISTING METHODS The presented method has limitations related to FFT resolution and univariate models, but it is computationally simple and allows obtaining a low-complex stochastic model of the EEG/MEG spectrum variability. CONCLUSIONS Although physiological short-time MEG signals are in principle nonstationary in time, their power spectrum at the dominant (alpha) frequencies varies as a weakly stationary process. The proposed methodology has possible applications in prediction of EEG/MEG spectral properties in theoretical and clinical neuroscience.
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Affiliation(s)
- Lech Kipiński
- Department of Pathophysiology, Wrocław Medical University, 50-367 Wrocław, Poland.
| | - Wojciech Kordecki
- The Witelon State University of Applied Sciences in Legnica, 59-220 Legnica, Poland.
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23
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Knierim MT, Berger C, Reali P. Open-source concealed EEG data collection for Brain-computer-interfaces - neural observation through OpenBCI amplifiers with around-the-ear cEEGrid electrodes. BRAIN-COMPUTER INTERFACES 2021. [DOI: 10.1080/2326263x.2021.1972633] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Michael Thomas Knierim
- Institute of Information Systems and Marketing (IISM, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Christoph Berger
- Institute of Information Systems and Marketing (IISM, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Pierluigi Reali
- Department of Electronics, Information, and Bioengineering, Politecnico Di Milano, Milan, Italy
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24
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De Pretto M, Mouthon M, Debove I, Pollo C, Schüpbach M, Spierer L, Accolla EA. Proactive inhibition is not modified by deep brain stimulation for Parkinson's disease: An electrical neuroimaging study. Hum Brain Mapp 2021; 42:3934-3949. [PMID: 34110074 PMCID: PMC8288097 DOI: 10.1002/hbm.25530] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 04/23/2021] [Accepted: 05/03/2021] [Indexed: 11/06/2022] Open
Abstract
In predictable contexts, motor inhibitory control can be deployed before the actual need for response suppression. The brain functional underpinnings of proactive inhibition, and notably the role of basal ganglia, are not entirely identified. We investigated the effects of deep brain stimulation of the subthalamic nucleus or internal globus pallidus on proactive inhibition in patients with Parkinson's disease. They completed a cued go/no-go proactive inhibition task ON and (unilateral) OFF stimulation while EEG was recorded. We found no behavioural effect of either subthalamic nucleus or internal globus pallidus deep brain stimulation on proactive inhibition, despite a general improvement of motor performance with subthalamic nucleus stimulation. In the non-operated and subthalamic nucleus group, we identified periods of topographic EEG modulation by the level of proactive inhibition. In the subthalamic nucleus group, source estimation analysis suggested the initial involvement of bilateral frontal and occipital areas, followed by a right lateralized fronto-basal network, and finally of right premotor and left parietal regions. Our results confirm the overall preservation of proactive inhibition capacities in both subthalamic nucleus and internal globus pallidus deep brain stimulation, and suggest a partly segregated network for proactive inhibition, with a preferential recruitment of the indirect pathway.
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Affiliation(s)
- Michael De Pretto
- Neurology Unit, Medicine Section, Faculty of Sciences and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Michael Mouthon
- Neurology Unit, Medicine Section, Faculty of Sciences and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Ines Debove
- Movement Disorders Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Claudio Pollo
- Department of Neurosurgery, Inselspital University Hospital Bern, Bern, Switzerland
| | - Michael Schüpbach
- Movement Disorders Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Lucas Spierer
- Neurology Unit, Medicine Section, Faculty of Sciences and Medicine, University of Fribourg, Fribourg, Switzerland
| | - Ettore A Accolla
- Neurology Unit, Medicine Section, Faculty of Sciences and Medicine, University of Fribourg, Fribourg, Switzerland.,Neurology Unit, Department of Medicine, HFR - Cantonal Hospital Fribourg, Fribourg, Switzerland
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25
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Jurgiel J, Miyakoshi M, Dillon A, Piacentini J, Makeig S, Loo SK. Inhibitory control in children with tic disorder: aberrant fronto-parietal network activity and connectivity. Brain Commun 2021; 3:fcab067. [PMID: 33977267 PMCID: PMC8093924 DOI: 10.1093/braincomms/fcab067] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/23/2021] [Accepted: 02/26/2021] [Indexed: 12/03/2022] Open
Abstract
Chronic tic disorders, including Tourette syndrome, are typically thought to have deficits in cognitive inhibition and top down cognitive control due to the frequent and repetitive occurrence of tics, yet studies reporting task performance results have been equivocal. Despite similar behavioural performance, individuals with chronic tic disorder have exhibited aberrant patterns of neural activation in multiple frontal and parietal regions relative to healthy controls during inhibitory control paradigms. In addition to these top down attentional control regions, widespread alterations in brain activity across multiple neural networks have been reported. There is a dearth, however, of studies examining event-related connectivity during cognitive inhibitory paradigms among affected individuals. The goal of this study was to characterize neural oscillatory activity and effective connectivity, using a case–control design, among children with and without chronic tic disorder during performance of a cognitive inhibition task. Electroencephalogram data were recorded in a cohort of children aged 8–12 years old (60 with chronic tic disorder, 35 typically developing controls) while they performed a flanker task. While task accuracy did not differ by diagnosis, children with chronic tic disorder displayed significant cortical source-level, event-related spectral power differences during incongruent flanker trials, which required inhibitory control. Specifically, attenuated broad band oscillatory power modulation within the anterior cingulate cortex was observed relative to controls. Whole brain effective connectivity analyses indicated that children with chronic tic disorder exhibit greater information flow between the anterior cingulate and other fronto-parietal network hubs (midcingulate cortex and precuneus) relative to controls, who instead showed stronger connectivity between central and posterior nodes. Spectral power within the anterior cingulate was not significantly correlated with any connectivity edges, suggesting lower power and higher connectivity are independent (versus resultant) neural mechanisms. Significant correlations between clinical features, task performance and anterior cingulate spectral power and connectivity suggest this region is associated with tic impairment (r = −0.31, P = 0.03) and flanker task incongruent trial accuracy (r’s = −0.27 to −0.42, P’s = 0.0008–0.04). Attenuated activation of the anterior cingulate along with dysregulated information flow between and among nodes within the fronto-parietal attention network may be neural adaptations that result from frequent engagement of neural pathways needed for inhibitory control in chronic tic disorder.
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Affiliation(s)
- Joseph Jurgiel
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Makoto Miyakoshi
- Swartz Center for Neural Computation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Andrea Dillon
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - John Piacentini
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Scott Makeig
- Swartz Center for Neural Computation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sandra K Loo
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA 90095, USA
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26
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Feasibility of combining functional near-infrared spectroscopy with electroencephalography to identify chronic stroke responders to cerebellar transcranial direct current stimulation-a computational modeling and portable neuroimaging methodological study. THE CEREBELLUM 2021; 20:853-871. [PMID: 33675516 DOI: 10.1007/s12311-021-01249-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/21/2021] [Indexed: 10/22/2022]
Abstract
Feasibility of portable neuroimaging of cerebellar transcranial direct current stimulation (ctDCS) effects on the cerebral cortex has not been investigated vis-à-vis cerebellar lobular electric field strength. We studied functional near-infrared spectroscopy (fNIRS) in conjunction with electroencephalography (EEG) to measure changes in the brain activation at the prefrontal cortex (PFC) and the sensorimotor cortex (SMC) following ctDCS as well as virtual reality-based balance training (VBaT) before and after ctDCS treatment in 12 hemiparetic chronic stroke survivors. We performed general linear modeling (GLM) that putatively associated the lobular electric field strength with the changes in the fNIRS-EEG measures at the ipsilesional and contra-lesional PFC and SMC. Here, fNIRS-EEG measures were found in the latent space from canonical correlation analysis (CCA) between the changes in total hemoglobin (tHb) concentrations (0.01-0.07Hz and 0.07-0.13Hz bands) and log10-transformed EEG bandpower within 1-45 Hz where significant (Wilks' lambda>0.95) canonical correlations were found only for the 0.07-0.13-Hz band. Also, the first principal component (97.5% variance accounted for) of the mean lobular electric field strength was a good predictor of the latent variables of oxy-hemoglobin (O2Hb) concentrations and log10-transformed EEG bandpower. GLM also provided insights into non-responders to ctDCS who also performed poorly in the VBaT due to ideomotor apraxia. Future studies should investigate fNIRS-EEG joint-imaging in a larger cohort to identify non-responders based on GLM fitting to the fNIRS-EEG data.
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Miyakoshi M, Schmitt LM, Erickson CA, Sweeney JA, Pedapati EV. Can We Push the "Quasi-Perfect Artifact Rejection" Even Closer to Perfection? Front Neuroinform 2021; 14:597079. [PMID: 33584237 PMCID: PMC7873913 DOI: 10.3389/fninf.2020.597079] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 12/16/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Makoto Miyakoshi
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, United States
| | - Lauren M Schmitt
- Developmental and Behavioral Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Craig A Erickson
- Divisions of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - John A Sweeney
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Ernest V Pedapati
- Divisions of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Divisions of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
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Yue K, Wang D, Chiu SC, Liu Y. Investigate the 3D Visual Fatigue Using Modified Depth-Related Visual Evoked Potential Paradigm. IEEE Trans Neural Syst Rehabil Eng 2021; 28:2794-2804. [PMID: 33406041 DOI: 10.1109/tnsre.2021.3049566] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Prolonged viewing of 3D content may result in severe fatigue symptoms, giving negative user experience thus hindering the development of 3D industry. For 3D visual fatigue evaluation, previous studies focused on exploring the changes of frequency-domain features in EEG for various fatigue degrees. However, their time-domain features were scarcely investigated. In this study, a modified paradigm with a random disparities order is adopted to evoke the depth-related visual evoked potentials (DVEPs). Then the characteristics of the DVEPs components for various fatigue degrees are compared using one-way repeated-measurement ANOVA. Point-by-point permutation statistics revealed sample points from 100ms to 170ms - including P1 and N1 - in sensors Pz and P4 changed significantly with visual fatigue. More specifically, we find that the amplitudes of P1 and N1 change significantly when visual fatigue increases. Additionally, independent component analysis identify P1 and N1 which originate from posterior cingulate cortex are associated statistically with 3D visual fatigue. Our results indicate there is a significant correlation between 3D visual fatigue and P1 amplitude, as well as N1, of DVEPs on right parietal areas. We believe the characteristics (e.g., amplitude and latency) of identified components may be the indicators of 3D visual fatigue evaluation. Furthermore, we argue that 3D visual fatigue may be associated with the activities decrease of the attention and the processing capacity of disparity.
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Tran XA, McDonald N, Dickinson A, Scheffler A, Frohlich J, Marin A, Kure Liu C, Nosco E, Şentürk D, Dapretto M, Spurling Jeste S. Functional connectivity during language processing in 3-month-old infants at familial risk for autism spectrum disorder. Eur J Neurosci 2020; 53:1621-1637. [PMID: 33043498 DOI: 10.1111/ejn.15005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 09/05/2020] [Accepted: 10/06/2020] [Indexed: 11/27/2022]
Abstract
Auditory statistical learning (ASL) plays a role in language development and may lay a foundation for later social communication impairment. As part of a longitudinal study of infant siblings, we asked whether electroencephalography (EEG) measures of connectivity during ASL at 3 months of age-differentiated infants who showed signs of autism spectrum disorder (ASD) at age 18 months. We measured spectral power and phase coherence in the theta (4-6 Hz) and alpha (6-12 Hz) frequency bands within putative language networks. Infants were divided into ASD-concern (n = 14) and No-ASD-concern (n = 49) outcome groups based on their ASD symptoms at 18 months, measured using the Autism Diagnostic Observation Scale Toddler Module. Using permutation testing, we identified a trend toward reduced left fronto-central phase coherence at the electrode pair F9-C3 in both theta and alpha frequency bands in infants who later showed ASD symptoms at 18 months. Across outcome groups, alpha coherence at 3 months correlated with greater word production at 18 months on the MacArthur-Bates Communicative Development Inventory. This study introduces signal processing and analytic tools that account for the challenges inherent in infant EEG studies, such as short duration of recordings, considerable movement artifact, and variable volume conduction. Our results indicate that connectivity, as measured by phase coherence during 2.5 min of ASL, can be quantified as early as 3 months and suggest that early alternations in connectivity may serve as markers of resilience for neurodevelopmental impairments.
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Affiliation(s)
- Xuan A Tran
- Center for Autism Research and Treatment, UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | - Nicole McDonald
- Center for Autism Research and Treatment, UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | - Abigail Dickinson
- Center for Autism Research and Treatment, UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | - Aaron Scheffler
- Center for Autism Research and Treatment, UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | - Joel Frohlich
- Center for Autism Research and Treatment, UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | - Andrew Marin
- Center for Autism Research and Treatment, UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | - Christopher Kure Liu
- Center for Autism Research and Treatment, UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | - Erin Nosco
- Center for Autism Research and Treatment, UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | - Damla Şentürk
- Center for Autism Research and Treatment, UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | - Mirella Dapretto
- Center for Autism Research and Treatment, UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | - Shafali Spurling Jeste
- Center for Autism Research and Treatment, UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
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Nenna F, Do CT, Protzak J, Gramann K. Alteration of brain dynamics during dual-task overground walking. Eur J Neurosci 2020; 54:8158-8174. [PMID: 32881128 DOI: 10.1111/ejn.14956] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 08/25/2020] [Accepted: 08/26/2020] [Indexed: 11/29/2022]
Abstract
When walking in our natural environment, we often solve additional cognitive tasks. This increases the demand of resources needed for both the cognitive and motor systems, resulting in Cognitive-Motor Interference (CMI). A large portion of neurophysiological investigations on CMI took place in static settings, emphasizing the experimental rigor but overshadowing the ecological validity. As a more ecologically valid alternative to treadmill and desktop-based setups to investigate CMI, we developed a dual-task walking scenario in virtual reality (VR) combined with Mobile Brain/Body Imaging (MoBI). We aimed at investigating how brain dynamics are modulated by dual-task overground walking with an additional task in the visual domain. Participants performed a visual discrimination task in VR while standing (single-task) and walking overground (dual-task). Even though walking had no impact on the performance in the visual discrimination task, a P3 amplitude reduction along with changes in power spectral densities (PSDs) were observed for discriminating visual stimuli during dual-task walking. These results reflect an impact of walking on the parallel processing of visual stimuli even when the cognitive task is particularly easy. This standardized and easy to modify VR paradigm helps to systematically study CMI, allowing researchers to control for the impact of additional task complexity of tasks in different sensory modalities. Future investigations implementing an improved virtual design with more challenging cognitive and motor tasks will have to investigate the roles of both cognition and motion, allowing for a better understanding of the functional architecture of attention reallocation between cognitive and motor systems during active behavior.
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Affiliation(s)
- Federica Nenna
- Department of General Psychology, University of Padova, Padova, Italy
| | - Cao Tri Do
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland
| | - Janna Protzak
- Junior research group FANS (Pedestrian Assistance System for Older Road User), Berlin Institute of Technology, Berlin, Germany
| | - Klaus Gramann
- Biological Psychology and Neuroergonomics, Berlin Institute of Technology, Berlin, Germany.,School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia.,Center for Advanced Neurological Engineering, University of California, San Diego, CA, USA
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31
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Coffman BA, Haas G, Olson C, Cho R, Ghuman AS, Salisbury DF. Reduced Dorsal Visual Oscillatory Activity During Working Memory Maintenance in the First-Episode Schizophrenia Spectrum. Front Psychiatry 2020; 11:743. [PMID: 32848922 PMCID: PMC7417606 DOI: 10.3389/fpsyt.2020.00743] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 07/16/2020] [Indexed: 11/17/2022] Open
Abstract
Cognitive deficits in people with schizophrenia are among the hardest to treat and strongly predict functional outcome. The ability to maintain sensory precepts in memory over a short delay is impacted early in the progression of schizophrenia and has been linked to reliable neurophysiological markers. Yet, little is known about the mechanisms of these deficits. Here, we investigated possible neurophysiological mechanisms of impaired visual short-term memory (vSTM, aka working memory maintenance) in the first-episode schizophrenia spectrum (FESz) using magnetoencephalography (MEG). Twenty-eight FESz and 25 matched controls performed a lateralized change detection task where they were cued to selectively attend and remember colors of circles presented in either the left or right peripheral visual field over a 1 s delay. Contralateral alpha suppression (CAS) during the delay period was used to assess selective attention to cued visual hemifields held in vSTM. Delay-period CAS was compared between FESz and controls and between trials presenting one vs three items per visual hemifield. CAS in dorsal visual cortex was reduced in FESz compared to controls in high-load trials, but not low-load trials. Group differences in CAS were found beginning 100 ms after the disappearance of the memory set, suggesting deficits were not due to the initial deployment of attention to the cued visual hemifield prior to stimulus presentation. CAS was not greater for high-load vs low-load trials in FESz subjects, although this effect was prominent in controls. Further, lateralized gamma (34-40 Hz) power emerged in dorsal visual cortex prior to the onset of CAS in controls but not FESz. Gamma power in this cluster differed between groups at both high and low load. CAS deficits observed in FESz were correlated with change detection accuracy, working memory function, estimated IQ, and negative symptoms. Our results implicate deficits in CAS in trials requiring broad, but not narrow, focus of attention to spatially distributed objects maintained in vSTM in FESz, possibly due to reduced ability to broadly distribute visuospatial attention (alpha) or disruption of object-location binding (gamma) during encoding/consolidation. This early pathophysiology may shed light upon mechanisms of emerging working memory deficits that are intrinsic to schizophrenia.
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Affiliation(s)
- Brian A. Coffman
- Clinical Neurophysiology Research Laboratory, Western Psychiatric Hospital of UPMC, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Gretchen Haas
- Western Psychiatric Hospital of UPMC, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Carl Olson
- Center for Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Raymond Cho
- Western Psychiatric Hospital of UPMC, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Avniel Singh Ghuman
- Laboratory of Cognitive Neurodynamics, Department of Neurosurgery, Presbyterian Hospital, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Dean F. Salisbury
- Clinical Neurophysiology Research Laboratory, Western Psychiatric Hospital of UPMC, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
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Hasan SMS, Siddiquee MR, Bai O. Asynchronous Prediction of Human Gait Intention in a Pseudo Online Paradigm Using Wavelet Transform. IEEE Trans Neural Syst Rehabil Eng 2020; 28:1623-1635. [PMID: 32634099 DOI: 10.1109/tnsre.2020.2998778] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Prediction of human voluntary gait intention is a very significant task to ensure direct cortical control of real-life assistive technologies for locomotion rehabilitation. Neurophysiological studies provide that human voluntary gait intention is represented by slow DC potentials and power shifts in specific frequency ranges of brain wave, which can be detected 1.5- 2 seconds before the actual onset. The goal of this study was to determine whether it is possible to reliably detect the intention of voluntary gait 'starting' and 'stopping' intention before it takes place. A computational algorithm was designed to implement asynchronous prediction of gait intention in an offline and pseudo-online environment using support vector machine. Six healthy subjects participated in the study and performed self- paced voluntary gait cycles. A combination of advanced wavelet transform algorithms resulted in 88.23± 1.59% accuracy, 85.42± 4.03% sensitivity and 90.24± 2.78% specificity for intention of start detection and 87.04± 1.72% accuracy, 82.69± 4.13% sensitivity and 89.59± 3.04% specificity for intention to stop walking in offline testing. Additionally, the wavelet transform methods accompanied with threshold regulation and majority voting algorithm resulted in a True Positive Rate of 85.5± 5.0% and 81.2± 3.3% for 'start' and 'stop' prediction with 6.8± 0.7 and 9.4± 1.0 False Positives per Minute respectively in pseudo online testing. The average detection latencies were -1002 ± 603 ms and -943 ± 603 ms, respectively, for 'start' and 'stop' prediction. The study provides promising outcomes in terms of TPR, FP/min, and detection latency, which suggests that human voluntary gait intention can be predicted before the onset of movement.
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Ko LW, Chikara RK, Lee YC, Lin WC. Exploration of User's Mental State Changes during Performing Brain-Computer Interface. SENSORS 2020; 20:s20113169. [PMID: 32503162 PMCID: PMC7308896 DOI: 10.3390/s20113169] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 05/24/2020] [Accepted: 05/28/2020] [Indexed: 01/27/2023]
Abstract
Substantial developments have been established in the past few years for enhancing the performance of brain–computer interface (BCI) based on steady-state visual evoked potential (SSVEP). The past SSVEP-BCI studies utilized different target frequencies with flashing stimuli in many different applications. However, it is not easy to recognize user’s mental state changes when performing the SSVEP-BCI task. What we could observe was the increasing EEG power of the target frequency from the user’s visual area. BCI user’s cognitive state changes, especially in mental focus state or lost-in-thought state, will affect the BCI performance in sustained usage of SSVEP. Therefore, how to differentiate BCI users’ physiological state through exploring their neural activities changes while performing SSVEP is a key technology for enhancing the BCI performance. In this study, we designed a new BCI experiment which combined working memory task into the flashing targets of SSVEP task using 12 Hz or 30 Hz frequencies. Through exploring the EEG activity changes corresponding to the working memory and SSVEP task performance, we can recognize if the user’s cognitive state is in mental focus or lost-in-thought. Experiment results show that the delta (1–4 Hz), theta (4–7 Hz), and beta (13–30 Hz) EEG activities increased more in mental focus than in lost-in-thought state at the frontal lobe. In addition, the powers of the delta (1–4 Hz), alpha (8–12 Hz), and beta (13–30 Hz) bands increased more in mental focus in comparison with the lost-in-thought state at the occipital lobe. In addition, the average classification performance across subjects for the KNN and the Bayesian network classifiers were observed as 77% to 80%. These results show how mental state changes affect the performance of BCI users. In this work, we developed a new scenario to recognize the user’s cognitive state during performing BCI tasks. These findings can be used as the novel neural markers in future BCI developments.
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Affiliation(s)
- Li-Wei Ko
- Department of Biological Science and Technology, College of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan;
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Chiao Tung University, Hsinchu 300, Taiwan
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Correspondence: (L.-W.K.); (W.-C.L.)
| | - Rupesh Kumar Chikara
- Department of Biological Science and Technology, College of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan;
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Chiao Tung University, Hsinchu 300, Taiwan
| | - Yi-Chieh Lee
- Department of Computer Science, National Chiao Tung University, Hsinchu 300, Taiwan;
| | - Wen-Chieh Lin
- Department of Computer Science, National Chiao Tung University, Hsinchu 300, Taiwan;
- Correspondence: (L.-W.K.); (W.-C.L.)
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Othman MH, Bhattacharya M, Møller K, Kjeldsen S, Grand J, Kjaergaard J, Dutta A, Kondziella D. Resting-State NIRS-EEG in Unresponsive Patients with Acute Brain Injury: A Proof-of-Concept Study. Neurocrit Care 2020; 34:31-44. [PMID: 32333214 DOI: 10.1007/s12028-020-00971-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
BACKGROUND Neurovascular-based imaging techniques such as functional MRI (fMRI) may reveal signs of consciousness in clinically unresponsive patients but are often subject to logistical challenges in the intensive care unit (ICU). Near-infrared spectroscopy (NIRS) is another neurovascular imaging technique but low cost, can be performed serially at the bedside, and may be combined with electroencephalography (EEG), which are important advantages compared to fMRI. Combined NIRS-EEG, however, has never been evaluated for the assessment of neurovascular coupling and consciousness in acute brain injury. METHODS We explored resting-state oscillations in eight-channel NIRS oxyhemoglobin and eight-channel EEG band-power signals to assess neurovascular coupling, the prerequisite for neurovascular-based imaging detection of consciousness, in patients with acute brain injury in the ICU (n = 9). Conscious neurological patients from step-down units and wards served as controls (n = 14). Unsupervised adaptive mixture-independent component analysis (AMICA) was used to correlate NIRS-EEG data with levels of consciousness and clinical outcome. RESULTS Neurovascular coupling between NIRS oxyhemoglobin (0.07-0.13 Hz) and EEG band-power (1-12 Hz) signals at frontal areas was sensitive and prognostic to changing consciousness levels. AMICA revealed a mixture of five models from EEG data, with the relative probabilities of these models reflecting levels of consciousness over multiple days, although the accuracy was less than 85%. However, when combined with two channels of bilateral frontal neurovascular coupling, weighted k-nearest neighbor classification of AMICA probabilities distinguished unresponsive patients from conscious controls with > 90% accuracy (positive predictive value 93%, false discovery rate 7%) and, additionally, identified patients who subsequently failed to recover consciousness with > 99% accuracy. DISCUSSION We suggest that NIRS-EEG for monitoring of acute brain injury in the ICU is worthy of further exploration. Normalization of neurovascular coupling may herald recovery of consciousness after acute brain injury.
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Affiliation(s)
- Marwan H Othman
- Department of Neurology, Rigshospitalet, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Mahasweta Bhattacharya
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Kirsten Møller
- Department of Neuroanesthesiology, Copenhagen University Hospital, Copenhagen, Denmark.,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Søren Kjeldsen
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Johannes Grand
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Jesper Kjaergaard
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Anirban Dutta
- Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Daniel Kondziella
- Department of Neurology, Rigshospitalet, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark. .,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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Shafiul Hasan SM, Siddiquee MR, Atri R, Ramon R, Marquez JS, Bai O. Prediction of gait intention from pre-movement EEG signals: a feasibility study. J Neuroeng Rehabil 2020; 17:50. [PMID: 32299460 PMCID: PMC7164221 DOI: 10.1186/s12984-020-00675-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 04/01/2020] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Prediction of Gait intention from pre-movement Electroencephalography (EEG) signals is a vital step in developing a real-time Brain-computer Interface (BCI) for a proper neuro-rehabilitation system. In that respect, this paper investigates the feasibility of a fully predictive methodology to detect the intention to start and stop a gait cycle by utilizing EEG signals obtained before the event occurrence. METHODS An eight-channel, custom-made, EEG system with electrodes placed around the sensorimotor cortex was used to acquire EEG data from six healthy subjects and two amputees. A discrete wavelet transform-based method was employed to capture event related information in alpha and beta bands in the time-frequency domain. The Hjorth parameters, namely activity, mobility, and complexity, were extracted as features while a two-sample unpaired Wilcoxon test was used to get rid of redundant features for better classification accuracy. The feature set thus obtained was then used to classify between 'walk vs. stop' and 'rest vs. start' classes using support vector machine (SVM) classifier with RBF kernel in a ten-fold cross-validation scheme. RESULTS Using a fully predictive intention detection system, 76.41±4.47% accuracy, 72.85±7.48% sensitivity, and 79.93±5.50% specificity were achieved for 'rest vs. start' classification. While for 'walk vs. stop' classification, the obtained mean accuracy, sensitivity, and specificity were 74.12±4.12%, 70.24±6.45%, and 77.78±7.01% respectively. Overall average True Positive Rate achieved by this methodology was 72.06±8.27% with 1.45 False Positives/min. CONCLUSION Extensive simulations and resulting classification results show that it is possible to achieve statistically similar intention detection accuracy using either only pre-movement EEG features or trans-movement EEG features. The classifier performance shows the potential of the proposed methodology to predict human movement intention exclusively from the pre-movement EEG signal to be applied in real-life prosthetic and neuro-rehabilitation systems.
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Affiliation(s)
- S. M. Shafiul Hasan
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
| | - Masudur R. Siddiquee
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
| | - Roozbeh Atri
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
| | - Rodrigo Ramon
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
| | - J. Sebastian Marquez
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
| | - Ou Bai
- Department of Electrical and Computer Engineering, Florida International University, Miami, Florida USA
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36
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Tripanpitak K, Viriyavit W, Huang SY, Yu W. Classification of Pain Event Related Potential for Evaluation of Pain Perception Induced by Electrical Stimulation. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1491. [PMID: 32182766 PMCID: PMC7085779 DOI: 10.3390/s20051491] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 12/30/2019] [Accepted: 01/04/2020] [Indexed: 12/11/2022]
Abstract
Variability in individual pain sensitivity is a major problem in pain assessment. There have been studies reported using pain-event related potential (pain-ERP) for evaluating pain perception. However, none of them has achieved high accuracy in estimating multiple pain perception levels. A major reason lies in the lack of investigation of feature extraction. The goal of this study is to assess four different pain perception levels through classification of pain-ERP, elicited by transcutaneous electrical stimulation on healthy subjects. Nonlinear methods: Higuchi's fractal dimension, Grassberger-Procaccia correlation dimension, with auto-correlation, and moving variance functions were introduced into the feature extraction. Fisher score was used to select the most discriminative channels and features. As a result, the correlation dimension with a moving variance without channel selection achieved the best accuracies of 100% for both the two-level and the three-level classification but degraded to 75% for the four-level classification. The best combined feature group is the variance-based one, which achieved accuracy of 87.5% and 100% for the four-level and three-level classification, respectively. Moreover, the features extracted from less than 20 trials could not achieve sensible accuracy, which makes it difficult for an instantaneous pain perception levels evaluation. These results show strong evidence on the possibility of objective pain assessment using nonlinear feature-based classification of pain-ERP.
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Affiliation(s)
- Kornkanok Tripanpitak
- Department of Medical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba 263-8522, Japan; (K.T.); (W.V.)
| | - Waranrach Viriyavit
- Department of Medical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba 263-8522, Japan; (K.T.); (W.V.)
- School of ICT, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand
| | - Shao Ying Huang
- Engineering Product Design, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore;
| | - Wenwei Yu
- Department of Medical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba 263-8522, Japan; (K.T.); (W.V.)
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
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Corticomuscular control of walking in older people and people with Parkinson's disease. Sci Rep 2020; 10:2980. [PMID: 32076045 PMCID: PMC7031238 DOI: 10.1038/s41598-020-59810-w] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 01/30/2020] [Indexed: 12/29/2022] Open
Abstract
Changes in human gait resulting from ageing or neurodegenerative diseases are multifactorial. Here we assess the effects of age and Parkinson’s disease (PD) on corticospinal activity recorded during treadmill and overground walking. Electroencephalography (EEG) from 10 electrodes and electromyography (EMG) from bilateral tibialis anterior muscles were acquired from 22 healthy young, 24 healthy older and 20 adults with PD. Event-related power, corticomuscular coherence (CMC) and inter-trial coherence were assessed for EEG from bilateral sensorimotor cortices and EMG during the double-support phase of the gait cycle. CMC and EMG power at low beta frequencies (13–21 Hz) was significantly decreased in older and PD participants compared to young people, but there was no difference between older and PD groups. Older and PD participants spent shorter time in the swing phase than young individuals. These findings indicate age-related changes in the temporal coordination of gait. The decrease in low-beta CMC suggests reduced cortical input to spinal motor neurons in older people during the double-support phase. We also observed multiple changes in electrophysiological measures at low-gamma frequencies during treadmill compared to overground walking, indicating task-dependent differences in corticospinal locomotor control. These findings may be affected by artefacts and should be interpreted with caution.
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38
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Zhao B, Zhang G, Dang J. Temporal-Spatial-Spectral Investigation of Brain Network Dynamics in Human Speech Perception. Brain Inform 2020. [DOI: 10.1007/978-3-030-59277-6_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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Bao SC, Leung WC, K Cheung VC, Zhou P, Tong KY. Pathway-specific modulatory effects of neuromuscular electrical stimulation during pedaling in chronic stroke survivors. J Neuroeng Rehabil 2019; 16:143. [PMID: 31744520 PMCID: PMC6862792 DOI: 10.1186/s12984-019-0614-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 10/24/2019] [Indexed: 12/25/2022] Open
Abstract
Background Neuromuscular electrical stimulation (NMES) is extensively used in stroke motor rehabilitation. How it promotes motor recovery remains only partially understood. NMES could change muscular properties, produce altered sensory inputs, and modulate fluctuations of cortical activities; but the potential contribution from cortico-muscular couplings during NMES synchronized with dynamic movement has rarely been discussed. Method We investigated cortico-muscular interactions during passive, active, and NMES rhythmic pedaling in healthy subjects and chronic stroke survivors. EEG (128 channels), EMG (4 unilateral lower limb muscles) and movement parameters were measured during 3 sessions of constant-speed pedaling. Sensory-level NMES (20 mA) was applied to the muscles, and cyclic stimulation patterns were synchronized with the EMG during pedaling cycles. Adaptive mixture independent component analysis was utilized to determine the movement-related electro-cortical sources and the source dipole clusters. A directed cortico-muscular coupling analysis was conducted between representative source clusters and the EMGs using generalized partial directed coherence (GPDC). The bidirectional GPDC was compared across muscles and pedaling sessions for post-stroke and healthy subjects. Results Directed cortico-muscular coupling of NMES cycling was more similar to that of active pedaling than to that of passive pedaling for the tested muscles. For healthy subjects, sensory-level NMES could modulate GPDC of both ascending and descending pathways. Whereas for stroke survivors, NMES could modulate GPDC of only the ascending pathways. Conclusions By clarifying how NMES influences neuromuscular control during pedaling in healthy and post-stroke subjects, our results indicate the potential limitation of sensory-level NMES in promoting sensorimotor recovery in chronic stroke survivors.
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Affiliation(s)
- Shi-Chun Bao
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Wing-Cheong Leung
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Vincent C K Cheung
- School of Biomedical Sciences, and The Gerald Choa Neuroscience Centre, The Chinese University of Hong Kong, Hong Kong, China.,The KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, The Chinese University of Hong Kong, Hong Kong, China
| | - Ping Zhou
- Department of Physical Medicine and Rehabilitation, The University of Texas Health Science Center at Houston, Houston, 77030, TX, USA.,TIRR Memorial Hermann Research Center, Houston, 77030, TX, USA
| | - Kai-Yu Tong
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China. .,Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong, China.
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Marini F, Lee C, Wagner J, Makeig S, Gola M. A comparative evaluation of signal quality between a research-grade and a wireless dry-electrode mobile EEG system. J Neural Eng 2019; 16:054001. [PMID: 31096191 DOI: 10.1088/1741-2552/ab21f2] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE Electroencephalography (EEG) is widely used by clinicians, scientists, engineers and other professionals worldwide, with an increasing number of low-cost, commercially-oriented EEG systems that have become available in recent years. One such system is the Cognionics Quick-20 (Cognionics Inc., San Diego, USA), which uses dry electrodes and offers the convenience of portability thanks to its built-in amplifier and wireless connection. Because of such characteristics, this system has been used in several applications for both clinical and basic research studies. However, an investigation of the quality of the signals that are recorded using this system has not yet been reported. APPROACH To bridge this gap, here we conducted a systematic comparison of signal quality between the Cognionics Quick-20 system and the Brain Products actiCAP/actiCHamp (Brain Products GmbH, Munich, Germany), a state-of-the-art, wet-electrode, research-oriented EEG system. Resting-state EEG data were recorded from twelve human participants at rest in eyes open and eyes closed conditions. For both systems we evaluated the similarity of mean recorded power spectral density, and detection of alpha suppression associated with eyes open relative to eyes closed. MAIN RESULTS Power spectral densities were highly correlated across systems, with only minor topographical variability across the scalp. Both systems recorded alpha suppression during eyes open relative to eyes closed conditions. SIGNIFICANCE These results attest to the robustness and reliability of the dry-electrode Cognionics system relatively to the widely used Brain Products laboratory EEG system, and thus validate its utility for clinical and basic research purposes, at least in studies in which participants do not move.
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Affiliation(s)
- Francesco Marini
- Swartz Center for Computational Neuroscience, University of California San Diego, La Jolla, CA, United States of America. Center for Neuromodulation, University of California San Diego, La Jolla, CA, United States of America
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Solon AJ, Lawhern VJ, Touryan J, McDaniel JR, Ries AJ, Gordon SM. Decoding P300 Variability Using Convolutional Neural Networks. Front Hum Neurosci 2019; 13:201. [PMID: 31258469 PMCID: PMC6587927 DOI: 10.3389/fnhum.2019.00201] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Accepted: 05/28/2019] [Indexed: 11/13/2022] Open
Abstract
Deep convolutional neural networks (CNN) have previously been shown to be useful tools for signal decoding and analysis in a variety of complex domains, such as image processing and speech recognition. By learning from large amounts of data, the representations encoded by these deep networks are often invariant to moderate changes in the underlying feature spaces. Recently, we proposed a CNN architecture that could be applied to electroencephalogram (EEG) decoding and analysis. In this article, we train our CNN model using data from prior experiments in order to later decode the P300 evoked response from an unseen, hold-out experiment. We analyze the CNN output as a function of the underlying variability in the P300 response and demonstrate that the CNN output is sensitive to the experiment-induced changes in the neural response. We then assess the utility of our approach as a means of improving the overall signal-to-noise ratio in the EEG record. Finally, we show an example of how CNN-based decoding can be applied to the analysis of complex data.
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Affiliation(s)
- Amelia J Solon
- Human Research and Engineering Directorate, U.S. Army Research Laboratory, Adelphi, MD, United States.,DCS Corporation, Alexandria, VA, United States
| | - Vernon J Lawhern
- Human Research and Engineering Directorate, U.S. Army Research Laboratory, Adelphi, MD, United States
| | - Jonathan Touryan
- Human Research and Engineering Directorate, U.S. Army Research Laboratory, Adelphi, MD, United States
| | - Jonathan R McDaniel
- Human Research and Engineering Directorate, U.S. Army Research Laboratory, Adelphi, MD, United States.,DCS Corporation, Alexandria, VA, United States
| | - Anthony J Ries
- Human Research and Engineering Directorate, U.S. Army Research Laboratory, Adelphi, MD, United States
| | - Stephen M Gordon
- Human Research and Engineering Directorate, U.S. Army Research Laboratory, Adelphi, MD, United States.,DCS Corporation, Alexandria, VA, United States
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