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Liu M, Yu C, Shi J, Xu Y, Li Z, Huang J, Si Z, Yao L, Yin K, Zhao Z. Effects of one-week bilateral cerebellar iTBS on resting-state functional brain network and multi-task attentional performance in healthy individuals: A randomized, sham-controlled trial. Neuroimage 2024; 295:120648. [PMID: 38761882 DOI: 10.1016/j.neuroimage.2024.120648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/29/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024] Open
Abstract
BACKGROUND Cerebellar intermittent theta burst stimulation (iTBS) modulates the excitability of the cerebral cortex and may enhance attentional performance. To date, few studies have conducted iTBS on healthy subjects for one week and used electroencephalography (EEG) to investigate the effect of multiple stimulation sessions on resting-state functional brain networks and the daily stimulation effect on attentional performance. METHODS 16 healthy subjects participated in a one-week experiment, receiving bilateral cerebellar iTBS or sham stimulation and engaging in multi-task attentional training. The primary measures were the one-week attentional performance and pre- and post-experiment resting-state EEG activities. Amplitude Envelope Correlation (AEC) was used to construct the functional connectivity in the eye-open (EO) and eye-closed (EC) phases. RESULTS At least three sessions of iTBS were required to enhance multi-task performance significantly, whereas only one or two sessions failed to elicit the improvement. Compared with the control group, iTBS induced significant changes in PSD, AEC functional connectivity, and AEC network properties during the EO phase, while it had little effect during the EC phase. During the EO phase, the network property changes of the iTBS subject were correlated with improved attentional performance. CONCLUSION The multi-task performance requires multiple stimulations to enhance. iTBS affects the resting-state alpha band brain activities during the EO rather than the EC phase. The AEC network properties may serve as a biomarker to assess the attentional potential of healthy subjects.
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Affiliation(s)
- Meiliang Liu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China.
| | - Chao Yu
- Nanjing Research Institute of Electronics Technology, Nanjing, China.
| | - Jinping Shi
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Yunfang Xu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Zijin Li
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Junhao Huang
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Zhengye Si
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Li Yao
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Kuiying Yin
- Nanjing Research Institute of Electronics Technology, Nanjing, China.
| | - Zhiwen Zhao
- School of Artificial Intelligence, Beijing Normal University, Beijing, China; Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, China.
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Zhang K, Hu X. Unsupervised separation of nonlinearly mixed event-related potentials using manifold clustering and non-negative matrix factorization. Comput Biol Med 2024; 178:108700. [PMID: 38852400 DOI: 10.1016/j.compbiomed.2024.108700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/12/2024] [Accepted: 06/01/2024] [Indexed: 06/11/2024]
Abstract
Event-related potentials (ERPs) can quantify brain responses to reveal the neural mechanisms of sensory perception. However, ERPs often reflect nonlinear mixture responses to multiple sources of sensory stimuli, and an accurate separation of the response to each stimulus remains a challenge. This study aimed to separate the ERP into nonlinearly mixed source components specific to individual stimuli. We developed an unsupervised learning method based on clustering of manifold structures of mixture signals combined with channel optimization for signal source reconstruction using non-negative matrix factorization (NMF). Specifically, we first implemented manifold learning based on Local Tangent Space Alignment (LTSA) to extract the spatial manifold structure of multi-resolution sub-signals separated via wavelet packet transform. We then used fuzzy entropy to extract the dynamical process of the manifold structures and performed a k-means clustering to separate different sources. Lastly, we used NMF to obtain the optimal contributions of multiple channels to ensure accurate source reconstructions. We evaluated our developed approach using a simulated ERP dataset with known ground truth of two components of ERP mixture signals. Our results show that the correlation coefficient between the reconstructed source signal and the true source signal was 92.8 % and that the separation accuracy in ERP amplitude was 91.6 %. The results show that our unsupervised separation approach can accurately separate ERP signals from nonlinear mixture source components. The outcomes provide a promising way to isolate brain responses to multiple stimulus sources during multisensory perception.
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Affiliation(s)
- Kai Zhang
- Department of Mechanical Engineering, Pennsylvania State University, University Park, USA
| | - Xiaogang Hu
- Department of Mechanical Engineering, Pennsylvania State University, University Park, USA; Department of Kinesiology, Pennsylvania State University, University Park, USA; Department of Physical Medicine & Rehabilitation, Pennsylvania State Hershey College of Medicine, USA; Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, USA; Center for Neural Engineering, Pennsylvania State University, University Park, USA.
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3
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Ji D, Xiao X, Wu J, He X, Zhang G, Guo R, Liu M, Xu M, Lin Q, Jung TP, Ming D. A user-friendly visual brain-computer interface based on high-frequency steady-state visual evoked fields recorded by OPM-MEG. J Neural Eng 2024; 21:036024. [PMID: 38812288 DOI: 10.1088/1741-2552/ad44d8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 04/29/2024] [Indexed: 05/31/2024]
Abstract
Objective. Magnetoencephalography (MEG) shares a comparable time resolution with electroencephalography. However, MEG excels in spatial resolution, enabling it to capture even the subtlest and weakest brain signals for brain-computer interfaces (BCIs). Leveraging MEG's capabilities, specifically with optically pumped magnetometers (OPM-MEG), proves to be a promising avenue for advancing MEG-BCIs, owing to its exceptional sensitivity and portability. This study harnesses the power of high-frequency steady-state visual evoked fields (SSVEFs) to build an MEG-BCI system that is flickering-imperceptible, user-friendly, and highly accurate.Approach.We have constructed a nine-command BCI that operates on high-frequency SSVEF (58-62 Hz with a 0.5 Hz interval) stimulation. We achieved this by placing the light source inside and outside the magnetic shielding room, ensuring compliance with non-magnetic and visual stimulus presentation requirements. Five participants took part in offline experiments, during which we collected six-channel multi-dimensional MEG signals along both the vertical (Z-axis) and tangential (Y-axis) components. Our approach leveraged the ensemble task-related component analysis algorithm for SSVEF identification and system performance evaluation.Main Results.The offline average accuracy of our proposed system reached an impressive 92.98% when considering multi-dimensional conjoint analysis using data from both theZandYaxes. Our method achieved a theoretical average information transfer rate (ITR) of 58.36 bits min-1with a data length of 0.7 s, and the highest individual ITR reached an impressive 63.75 bits min-1.Significance.This study marks the first exploration of high-frequency SSVEF-BCI based on OPM-MEG. These results underscore the potential and feasibility of MEG in detecting subtle brain signals, offering both theoretical insights and practical value in advancing the development and application of MEG in BCI systems.
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Affiliation(s)
- Dengpei Ji
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, People's Republic of China
| | - Xiaolin Xiao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, People's Republic of China
| | - Jieyu Wu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, People's Republic of China
| | - Xiang He
- College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
| | - Guiying Zhang
- College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
| | - Ruihan Guo
- College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
| | - Miao Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, People's Republic of China
| | - Minpeng Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, People's Republic of China
| | - Qiang Lin
- College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang, People's Republic of China
| | - Tzyy-Ping Jung
- Swartz Center for Computational Neuroscience Institute for Neural Computation, University of California San Diego, San Diego, CA, United States of America
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, People's Republic of China
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4
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Liu T, Wu Y, Ye A, Cao L, Cao Y. Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs. Front Hum Neurosci 2024; 18:1400077. [PMID: 38841120 PMCID: PMC11150693 DOI: 10.3389/fnhum.2024.1400077] [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: 03/13/2024] [Accepted: 05/08/2024] [Indexed: 06/07/2024] Open
Abstract
Background Channel selection has become the pivotal issue affecting the widespread application of non-invasive brain-computer interface systems in the real world. However, constructing suitable multi-objective problem models alongside effective search strategies stands out as a critical factor that impacts the performance of multi-objective channel selection algorithms. This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems. Methods In TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on different multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA. Results The performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the effectiveness of TS-MOEA. Conclusion The proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can effectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency.
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Affiliation(s)
- Tianyu Liu
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Yu Wu
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - An Ye
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Lei Cao
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Yongnian Cao
- Tiktok Incorporation, San Jose, CA, United States
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Ludwig S, Bakas S, Adamos DA, Laskaris N, Panagakis Y, Zafeiriou S. EEGminer: discovering interpretable features of brain activity with learnable filters. J Neural Eng 2024; 21:036010. [PMID: 38684154 DOI: 10.1088/1741-2552/ad44d7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 04/29/2024] [Indexed: 05/02/2024]
Abstract
Objective. The patterns of brain activity associated with different brain processes can be used to identify different brain states and make behavioural predictions. However, the relevant features are not readily apparent and accessible. Our aim is to design a system for learning informative latent representations from multichannel recordings of ongoing EEG activity.Approach: We propose a novel differentiable decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. Specifically, we introduce filters parameterized by generalized Gaussian functions that offer a smooth derivative for stable end-to-end model training and allow for learning interpretable features. For the feature module, we use signal magnitude and functional connectivity estimates.Main results.We demonstrate the utility of our model on a new EEG dataset of unprecedented size (i.e. 721 subjects), where we identify consistent trends of music perception and related individual differences. Furthermore, we train and apply our model in two additional datasets, specifically for emotion recognition on SEED and workload classification on simultaneous task EEG workload. The discovered features align well with previous neuroscience studies and offer new insights, such as marked differences in the functional connectivity profile between left and right temporal areas during music listening. This agrees with the specialisation of the temporal lobes regarding music perception proposed in the literature.Significance. The proposed method offers strong interpretability of learned features while reaching similar levels of accuracy achieved by black box deep learning models. This improved trustworthiness may promote the use of deep learning models in real world applications. The model code is available athttps://github.com/SMLudwig/EEGminer/.
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Affiliation(s)
- Siegfried Ludwig
- Department of Computing, Imperial College London, London SW7 2RH, United Kingdom
- Cogitat Ltd, London, United Kingdom
| | - Stylianos Bakas
- Department of Computing, Imperial College London, London SW7 2RH, United Kingdom
- School of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
- Cogitat Ltd, London, United Kingdom
| | - Dimitrios A Adamos
- Department of Computing, Imperial College London, London SW7 2RH, United Kingdom
- Cogitat Ltd, London, United Kingdom
| | - Nikolaos Laskaris
- School of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
- Cogitat Ltd, London, United Kingdom
| | - Yannis Panagakis
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens 15784, Greece
- Cogitat Ltd, London, United Kingdom
| | - Stefanos Zafeiriou
- Department of Computing, Imperial College London, London SW7 2RH, United Kingdom
- Cogitat Ltd, London, United Kingdom
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Wei X, Liao PC. Connecting the dots: Exploring brain connectivity during responsibility recognition in construction contract negotiations. Comput Biol Med 2024; 173:108347. [PMID: 38554663 DOI: 10.1016/j.compbiomed.2024.108347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/11/2024] [Accepted: 03/17/2024] [Indexed: 04/02/2024]
Abstract
Despite recent advancements in monitoring brain activity, causal relationships within the brain during responsibility identification in construction contracts remain unexplored. We aimed to understand the neural mechanisms involved in the cognitive components and their interactions related to contract text reading by delving into the brain mechanisms of contract responsibility identification. This study investigated students' brain connectivity using electroencephalography (EEG) data during a text-based contract responsibility-identification task. It employed an adaptive directed transfer function based on Granger causality to simulate directed and time-varying information flow in observed brain activity. We evaluated the EEG records of 18 participants under two reading conditions (involving or not involving contractor responsibility). During responsibility identification, the most substantial information exchange occurs in the somatosensory area of the brain. The results revealed a "top-down" cortical mechanism for responsibility identification, with the left parietal-occipital area (PO3) as the central hub promoting connectivity structures. These findings indicate that the perceptual processing of contract responsibility texts is associated with higher visual learning and memory quality. Contracts without contractor-responsibility clauses resulted in more substantial information flow output in the frontal cortex and consumed more cognitive resources. Our findings advance the understanding of cognitive processes involved in contract responsibility identification, providing a framework for investigating causal relationships within the brain and novel insights into cortical mechanisms. By identifying the neural basis of responsibility identification, stakeholders can develop effective training programs for negotiators and enhance their ability to interpret and implement construction contracts.
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Affiliation(s)
- Xinyan Wei
- Department of Construction Management, Tsinghua University, Beijing, 100084, China.
| | - Pin-Chao Liao
- Department of Construction Management, Tsinghua University, Beijing, 100084, China.
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7
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Acharya K, Olivares F, Zanin M. How representative are air transport functional complex networks? A quantitative validation. CHAOS (WOODBURY, N.Y.) 2024; 34:043133. [PMID: 38598674 DOI: 10.1063/5.0189642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 03/21/2024] [Indexed: 04/12/2024]
Abstract
Functional networks have emerged as powerful instruments to characterize the propagation of information in complex systems, with applications ranging from neuroscience to climate and air transport. In spite of their success, reliable methods for validating the resulting structures are still missing, forcing the community to resort to expert knowledge or simplified models of the system's dynamics. We here propose the use of a real-world problem, involving the reconstruction of the structure of flights in the US air transport system from the activity of individual airports, as a way to explore the limits of such an approach. While the true connectivity is known and is, therefore, possible to provide a quantitative benchmark, this problem presents challenges commonly found in other fields, including the presence of non-stationarities and observational noise, and the limitedness of available time series. We explore the impact of elements like the specific functional metric employed, the way of detrending the time series, or the size of the reconstructed system and discuss how the conclusions here drawn could have implications for similar analyses in neuroscience.
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Affiliation(s)
- Kishor Acharya
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122 Palma de Mallorca, Spain
| | - Felipe Olivares
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122 Palma de Mallorca, Spain
| | - Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122 Palma de Mallorca, Spain
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8
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Schüller A, Schilling A, Krauss P, Reichenbach T. The Early Subcortical Response at the Fundamental Frequency of Speech Is Temporally Separated from Later Cortical Contributions. J Cogn Neurosci 2024; 36:475-491. [PMID: 38165737 DOI: 10.1162/jocn_a_02103] [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: 01/04/2024]
Abstract
Most parts of speech are voiced, exhibiting a degree of periodicity with a fundamental frequency and many higher harmonics. Some neural populations respond to this temporal fine structure, in particular at the fundamental frequency. This frequency-following response to speech consists of both subcortical and cortical contributions and can be measured through EEG as well as through magnetoencephalography (MEG), although both differ in the aspects of neural activity that they capture: EEG is sensitive to both radial and tangential sources as well as to deep sources, whereas MEG is more restrained to the measurement of tangential and superficial neural activity. EEG responses to continuous speech have shown an early subcortical contribution, at a latency of around 9 msec, in agreement with MEG measurements in response to short speech tokens, whereas MEG responses to continuous speech have not yet revealed such an early component. Here, we analyze MEG responses to long segments of continuous speech. We find an early subcortical response at latencies of 4-11 msec, followed by later right-lateralized cortical activities at delays of 20-58 msec as well as potential subcortical activities. Our results show that the early subcortical component of the FFR to continuous speech can be measured from MEG in populations of participants and that its latency agrees with that measured with EEG. They furthermore show that the early subcortical component is temporally well separated from later cortical contributions, enabling an independent assessment of both components toward further aspects of speech processing.
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Affiliation(s)
| | | | - Patrick Krauss
- Friedrich-Alexander-Universität Erlangen-Nürnberg
- Universitätsklinikum Erlangen
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Amato LG, Vergani AA, Lassi M, Fabbiani C, Mazzeo S, Burali R, Nacmias B, Sorbi S, Mannella R, Grippo A, Bessi V, Mazzoni A. Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12526. [PMID: 38371358 PMCID: PMC10870085 DOI: 10.1002/dad2.12526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/13/2023] [Accepted: 12/19/2023] [Indexed: 02/20/2024]
Abstract
INTRODUCTION Early identification of Alzheimer's disease (AD) is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with AD are difficult to discern. METHODS We developed a cortical model of AD-related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. In a monocentric study we collected electroencephalography (EEG) recordings at rest from participants in healthy (HC, n = 17), subjective cognitive decline (SCD, n = 58), and mild cognitive impairment (MCI, n = 44) conditions. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings. RESULTS Our model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95 vs 0.75) but also in identifying SCD patients with biological hallmarks of AD in the cerebrospinal fluid (recall 0.87 vs 0.50). DISCUSSION Personalized models could (1) support classification of MCI, (2) assess the presence of AD pathology, and (3) estimate the risk of cognitive decline progression, based only on economical and non-invasive EEG recordings. Highlights Personalized cortical model estimating structural alterations from EEG recordings.Discrimination of Mild Cognitive Impairment (MCI) and Healthy (HC) subjects (95%)Prediction of biological markers of Alzheimer's in Subjective Decline (SCD) Subjects (87%)Transition correctly predicted for 3/3 subjects that converted from SCD to MCI after 1y.
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Affiliation(s)
- Lorenzo Gaetano Amato
- The BioRobotics InstituteSant'Anna School of Advanced StudiesPisaItaly
- Department of Excellence in Robotics and AISant'Anna School of Advanced StudiesPisaItaly
| | - Alberto Arturo Vergani
- The BioRobotics InstituteSant'Anna School of Advanced StudiesPisaItaly
- Department of Excellence in Robotics and AISant'Anna School of Advanced StudiesPisaItaly
| | - Michael Lassi
- The BioRobotics InstituteSant'Anna School of Advanced StudiesPisaItaly
- Department of Excellence in Robotics and AISant'Anna School of Advanced StudiesPisaItaly
| | - Carlo Fabbiani
- IRCSS Fondazione Don Carlo GnocchiFlorenceItaly
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | - Salvatore Mazzeo
- IRCSS Fondazione Don Carlo GnocchiFlorenceItaly
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | | | - Benedetta Nacmias
- IRCSS Fondazione Don Carlo GnocchiFlorenceItaly
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | - Sandro Sorbi
- IRCSS Fondazione Don Carlo GnocchiFlorenceItaly
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | | | | | - Valentina Bessi
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | - Alberto Mazzoni
- The BioRobotics InstituteSant'Anna School of Advanced StudiesPisaItaly
- Department of Excellence in Robotics and AISant'Anna School of Advanced StudiesPisaItaly
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Liuzzi P, Mannini A, Hakiki B, Campagnini S, Romoli AM, Draghi F, Burali R, Scarpino M, Cecchi F, Grippo A. Brain microstate spatio-temporal dynamics as a candidate endotype of consciousness. Neuroimage Clin 2023; 41:103540. [PMID: 38101096 PMCID: PMC10727951 DOI: 10.1016/j.nicl.2023.103540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 10/02/2023] [Accepted: 11/09/2023] [Indexed: 12/17/2023]
Abstract
Consciousness can be defined as a phenomenological experience continuously evolving. Current research showed how conscious mental activity can be subdivided into a series of atomic brain states converging to a discrete spatiotemporal pattern of global neuronal firing. Using the high temporal resolution of EEG recordings in patients with a severe Acquired Brain Injury (sABI) admitted to an Intensive Rehabilitation Unit (IRU), we detected a novel endotype of consciousness from the spatiotemporal brain dynamics identified via microstate analysis. Also, we investigated whether microstate features were associated with common neurophysiological alterations. Finally, the prognostic information comprised in such descriptors was analysed in a sub-cohort of patients with prolonged Disorder of Consciousness (pDoC). Occurrence of frontally-oriented microstates (C microstate), likelihood of maintaining such brain state or transitioning to the C topography and complexity were found to be indicators of consciousness presence and levels. Features of left-right asymmetric microstates and transitions toward them were found to be negatively correlated with antero-posterior brain reorganization and EEG symmetry. Substantial differences in microstates' sequence complexity and presence of C topography were found between groups of patients with alpha dominant background, cortical reactivity and antero-posterior gradient. Also, transitioning from left-right to antero-posterior microstates was found to be an independent predictor of consciousness recovery, stronger than consciousness levels at IRU's admission. In conclusions, global brain dynamics measured with scale-free estimators can be considered an indicator of consciousness presence and a candidate marker of short-term recovery in patients with a pDoC.
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Affiliation(s)
- Piergiuseppe Liuzzi
- IRCCS Don Carlo Gnocchi ONLUS, Firenze, Italy; Istituto di BioRobotica, Scuola Superiore Sant'Anna, Pontedera, Italy
| | | | | | | | | | | | | | | | - Francesca Cecchi
- IRCCS Don Carlo Gnocchi ONLUS, Firenze, Italy; Dipartimento di Medicina Sperimentale e Clinica, Università di Firenze, Firenze, Italy
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11
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Rodríguez-González V, Núñez P, Gómez C, Shigihara Y, Hoshi H, Tola-Arribas MÁ, Cano M, Guerrero Á, García-Azorín D, Hornero R, Poza J. Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses. Neuroimage 2023; 280:120332. [PMID: 37619796 DOI: 10.1016/j.neuroimage.2023.120332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 07/05/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023] Open
Abstract
The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as "canonical" frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the "Connectivity-based Meta-Bands" (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the "canonical" frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.
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Affiliation(s)
- Víctor Rodríguez-González
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain.
| | - Pablo Núñez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain
| | | | | | - Miguel Ángel Tola-Arribas
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; Servicio de Neurología. Hospital Universitario Río Hortega, Valladolid, Spain
| | - Mónica Cano
- Servicio de Neurología. Hospital Universitario Río Hortega, Valladolid, Spain
| | - Ángel Guerrero
- Hospital Clínico Universitario, Valladolid, Spain; Department of Medicine, University of Valladolid, Spain
| | | | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
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12
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Zandstra MG, Meijs H, Somers M, Stam CJ, de Wilde B, van Hecke J, Niemegeers P, Luykx JJ, van Dellen E. Associations between psychotropic drugs and rsEEG connectivity and network characteristics: a cross-sectional study in hospital-admitted psychiatric patients. Front Neurosci 2023; 17:1176825. [PMID: 37781262 PMCID: PMC10541222 DOI: 10.3389/fnins.2023.1176825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 08/22/2023] [Indexed: 10/03/2023] Open
Abstract
Introduction Resting-state EEG (rsEEG) characteristics, such as functional connectivity and network topology, are studied as potential biomarkers in psychiatric research. However, the presence of psychopharmacological treatment in study participants poses a potential confounding factor in biomarker research. To address this concern, our study aims to explore the impact of both single and multi-class psychotropic treatments on aforementioned rsEEG characteristics in a psychiatric population. Methods RsEEG was analyzed in a real-world cross-sectional sample of 900 hospital-admitted psychiatric patients. Patients were clustered into eight psychopharmacological groups: unmedicated, single-class treatment with antipsychotics (AP), antidepressants (AD) or benzodiazepines (BDZ), and multi-class combinations of these treatments. To assess the associations between psychotropic treatments and the macroscale rsEEG characteristics mentioned above, we employed a general linear model with post-hoc tests. Additionally, Spearman's rank correlation analyses were performed to explore potential dosage effects. Results Compared to unmedicated patients, single-class use of AD was associated with lower functional connectivity in the delta band, while AP was associated with lower functional connectivity in both the delta and alpha bands. Single-class use of BDZ was associated with widespread rsEEG differences, including lower functional connectivity across frequency bands and a different network topology within the beta band relative to unmedicated patients. All of the multi-class groups showed associations with functional connectivity or topology measures, but effects were most pronounced for concomitant use of all three classes of psychotropics. Differences were not only observed in comparison with unmedicated patients, but were also evident in comparisons between single-class, multi-class, and single/multi-class groups. Importantly, multi-class associations with rsEEG characteristics were found even in the absence of single-class associations, suggesting potential cumulative or interaction effects of different classes of psychotropics. Dosage correlations were only found for antipsychotics. Conclusion Our exploratory, cross-sectional study suggests small but significant associations between single and multi-class use of antidepressants, antipsychotics and benzodiazepines and macroscale rsEEG functional connectivity and network topology characteristics. These findings highlight the importance of considering the effects of specific psychotropics, as well as their interactions, when investigating rsEEG biomarkers in a medicated psychiatric population.
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Affiliation(s)
- Melissa G. Zandstra
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Hannah Meijs
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, Netherlands
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Metten Somers
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Bieke de Wilde
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Jan van Hecke
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Peter Niemegeers
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Jurjen J. Luykx
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, Netherlands
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Edwin van Dellen
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Neurology, Universitair Ziekenhuis (UZ), Brussels, Belgium
- Vrije Universiteit Brussel, Brussels, Belgium
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13
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Liu T, Ye A. Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems. Front Neurosci 2023; 17:1251968. [PMID: 37746153 PMCID: PMC10512944 DOI: 10.3389/fnins.2023.1251968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 08/21/2023] [Indexed: 09/26/2023] Open
Abstract
Background For non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem. Methods The multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space. Results The proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels. Conclusion The formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications.
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Affiliation(s)
- Tianyu Liu
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
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14
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Monroe DC, Berry NT, Fino PC, Rhea CK. A Dynamical Systems Approach to Characterizing Brain-Body Interactions during Movement: Challenges, Interpretations, and Recommendations. SENSORS (BASEL, SWITZERLAND) 2023; 23:6296. [PMID: 37514591 PMCID: PMC10385586 DOI: 10.3390/s23146296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023]
Abstract
Brain-body interactions (BBIs) have been the focus of intense scrutiny since the inception of the scientific method, playing a foundational role in the earliest debates over the philosophy of science. Contemporary investigations of BBIs to elucidate the neural principles of motor control have benefited from advances in neuroimaging, device engineering, and signal processing. However, these studies generally suffer from two major limitations. First, they rely on interpretations of 'brain' activity that are behavioral in nature, rather than neuroanatomical or biophysical. Second, they employ methodological approaches that are inconsistent with a dynamical systems approach to neuromotor control. These limitations represent a fundamental challenge to the use of BBIs for answering basic and applied research questions in neuroimaging and neurorehabilitation. Thus, this review is written as a tutorial to address both limitations for those interested in studying BBIs through a dynamical systems lens. First, we outline current best practices for acquiring, interpreting, and cleaning scalp-measured electroencephalography (EEG) acquired during whole-body movement. Second, we discuss historical and current theories for modeling EEG and kinematic data as dynamical systems. Third, we provide worked examples from both canonical model systems and from empirical EEG and kinematic data collected from two subjects during an overground walking task.
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Affiliation(s)
- Derek C Monroe
- Department of Kinesiology, University of North Carolina at Greensboro, Greensboro, NC 27402, USA
| | - Nathaniel T Berry
- Department of Kinesiology, University of North Carolina at Greensboro, Greensboro, NC 27402, USA
- Under Armour, Inc., Innovation, Baltimore, MD 21230, USA
| | - Peter C Fino
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA
| | - Christopher K Rhea
- College of Health Sciences, Old Dominion University, Norfolk, VA 23508, USA
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15
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Liao PC, Zhou X, Chong HY, Hu Y, Zhang D. Exploring construction workers' brain connectivity during hazard recognition: a cognitive psychology perspective. INTERNATIONAL JOURNAL OF OCCUPATIONAL SAFETY AND ERGONOMICS 2023; 29:207-215. [PMID: 35098890 DOI: 10.1080/10803548.2022.2035966] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Monitoring brain activity is a novel development for hazard recognition in the construction industry. However, very few empirical studies have investigated the causal connections within the brain. This study aimed to explore the brain connectivity of construction workers during hazard recognition. Electroencephalogram data were collected from construction workers to perform image-based hazard recognition tasks. The Granger causality-based adaptive directed transfer function was used to simulate directed and time-variant information flow across the observed brain activity from the perspective of cognitive psychology. The results suggested a top-down modulation of behavioral goals originating from the dorsal attention network during hazard relocation. The sensory cortex predominantly serves as the information outlet center and interacts extensively with the frontal and visual cortices, reflecting a top-down attention reorientation mechanism for processing threatening stimuli. Our findings of brain effective connectivity supplement new evidence underpinning parallel distributed processing theory for workplace hazard recognition.
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Affiliation(s)
- Pin-Chao Liao
- Department of Construction Management, Tsinghua University, China
| | - Xiaoshan Zhou
- Department of Construction Management, Tsinghua University, China
| | - Heap-Yih Chong
- School of Design and the Built Environment, Curtin University, Australia
| | - Yinan Hu
- Department of Construction Management, Tsinghua University, China
| | - Dan Zhang
- Department of Psychology, Tsinghua University, China
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16
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Ding X, Li X, Xu M, He Z, Jiang H. The effect of repetitive transcranial magnetic stimulation on electroencephalography microstates of patients with heroin-addiction. Psychiatry Res Neuroimaging 2023; 329:111594. [PMID: 36724624 DOI: 10.1016/j.pscychresns.2023.111594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/28/2022] [Accepted: 01/10/2023] [Indexed: 01/30/2023]
Abstract
The effects of transcranial magnetic stimulation in treating substance use disorders are gaining attention; however, most existing studies used subjective measures to examine the treatment effects. Objective electroencephalography (EEG)-based microstate analysis is important for measuring the efficacy of transcranial magnetic stimulation in patients with heroin addiction. We investigated dynamic brain activity changes in individuals with heroin addiction after transcranial magnetic stimulation using microstate indicators. Thirty-two patients received intermittent theta-burst stimulation (iTBS) over the left dorsolateral prefrontal cortex. Resting-state EEG data were collected pre-intervention and 10 days post-intervention. The feature values of the significantly different microstate classes were computed using a K-means clustering algorithm. Four EEG microstate classes (A-D) were noted. There were significant increases in the duration, occurrence, and contribution of microstate class A after the iTBS intervention. K-means classification accuracy reached 81.5%. The EEG microstate is an effective improvement indicator in patients with heroin addiction treated with iTBS. Microstates were examined using machine learning; this method effectively classified the pre- and post-intervention cohorts among patients with heroin addiction and healthy individuals. Using EEG microstate to measure heroin addiction and further exploring the effect of iTBS in patients with heroin addiction merit clinical investigation.
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Affiliation(s)
- Xiaobin Ding
- School of Psychology, Northwest Normal University, Lanzhou 730000, China
| | - Xiaoyan Li
- School of Psychology, Northwest Normal University, Lanzhou 730000, China.
| | - Ming Xu
- School of Psychology, Northwest Normal University, Lanzhou 730000, China
| | - Zijing He
- School of Psychology, Northwest Normal University, Lanzhou 730000, China
| | - Heng Jiang
- School of Psychology, Northwest Normal University, Lanzhou 730000, China
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17
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Mosayebi-Samani M, Agboada D, Mutanen TP, Haueisen J, Kuo MF, Nitsche MA. Transferability of cathodal tDCS effects from the primary motor to the prefrontal cortex: A multimodal TMS-EEG study. Brain Stimul 2023; 16:515-539. [PMID: 36828302 DOI: 10.1016/j.brs.2023.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 01/24/2023] [Accepted: 02/19/2023] [Indexed: 02/24/2023] Open
Abstract
Neurophysiological effects of transcranial direct current stimulation (tDCS) have been extensively studied over the primary motor cortex (M1). Much less is however known about its effects over non-motor areas, such as the prefrontal cortex (PFC), which is the neuronal foundation for many high-level cognitive functions and involved in neuropsychiatric disorders. In this study, we, therefore, explored the transferability of cathodal tDCS effects over M1 to the PFC. Eighteen healthy human participants (11 males and 8 females) were involved in eight randomized sessions per participant, in which four cathodal tDCS dosages, low, medium, and high, as well as sham stimulation, were applied over the left M1 and left PFC. After-effects of tDCS were evaluated via transcranial magnetic stimulation (TMS)-electroencephalography (EEG), and TMS-elicited motor evoked potentials (MEP), for the outcome parameters TMS-evoked potentials (TEP), TMS-evoked oscillations, and MEP amplitude alterations. TEPs were studied both at the regional and global scalp levels. The results indicate a regional dosage-dependent nonlinear neurophysiological effect of M1 tDCS, which is not one-to-one transferable to PFC tDCS. Low and high dosages of M1 tDCS reduced early positive TEP peaks (P30, P60), and MEP amplitudes, while an enhancement was observed for medium dosage M1 tDCS (P30). In contrast, prefrontal low, medium and high dosage tDCS uniformly reduced the early positive TEP peak amplitudes. Furthermore, for both cortical areas, regional tDCS-induced modulatory effects were not observed for late TEP peaks, nor TMS-evoked oscillations. However, at the global scalp level, widespread effects of tDCS were observed for both, TMS-evoked potentials and oscillations. This study provides the first direct physiological comparison of tDCS effects applied over different brain areas and therefore delivers crucial information for future tDCS applications.
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Affiliation(s)
- Mohsen Mosayebi-Samani
- Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany; Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| | - Desmond Agboada
- Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany; Institute of Psychology, Federal Armed Forces University Munich, Neubiberg, Germany
| | - Tuomas P Mutanen
- Department of Neuroscience & Biomedical Engineering, Aalto University, School of Science, 00076, Aalto, Espoo, Finland
| | - Jens Haueisen
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| | - Min-Fang Kuo
- Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany
| | - Michael A Nitsche
- Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany; Bielefeld University, University Hospital OWL, Protestant Hospital of Bethel Foundation, University Clinic of Psychiatry and Psychotherapy and University Clinic of Child and Adolescent Psychiatry and Psychotherapy, Bielefeld, Germany.
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18
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Nguyen KH, Ebbatson M, Tran Y, Craig A, Nguyen H, Chai R. Source-Space Brain Functional Connectivity Features in Electroencephalogram-Based Driver Fatigue Classification. SENSORS (BASEL, SWITZERLAND) 2023; 23:2383. [PMID: 36904587 PMCID: PMC10007183 DOI: 10.3390/s23052383] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/10/2023] [Accepted: 02/19/2023] [Indexed: 06/18/2023]
Abstract
This study examined the brain source space functional connectivity from the electroencephalogram (EEG) activity of 48 participants during a driving simulation experiment where they drove until fatigue developed. Source-space functional connectivity (FC) analysis is a state-of-the-art method for understanding connections between brain regions that may indicate psychological differences. Multi-band FC in the brain source space was constructed using the phased lag index (PLI) method and used as features to train an SVM classification model to classify driver fatigue and alert conditions. With a subset of critical connections in the beta band, a classification accuracy of 93% was achieved. Additionally, the source-space FC feature extractor demonstrated superiority over other methods, such as PSD and sensor-space FC, in classifying fatigue. The results suggested that source-space FC is a discriminative biomarker for detecting driving fatigue.
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Affiliation(s)
- Khanh Ha Nguyen
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
| | - Matthew Ebbatson
- School of Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia
| | - Yvonne Tran
- Department of Linguistics, Macquarie University Hearing, Macquarie University, Sydney, NSW 2109, Australia
| | - Ashley Craig
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- John Walsh Centre for Rehabilitation Research, Kolling Institute, Northern Sydney Local Health District, St Leonards, Sydney, NSW 2065, Australia
| | - Hung Nguyen
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
| | - Rifai Chai
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
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19
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Fang T, Wang J, Mu W, Song Z, Zhang X, Zhan G, Wang P, Bin J, Niu L, Zhang L, Kang X. Noninvasive neuroimaging and spatial filter transform enable ultra low delay motor imagery EEG decoding. J Neural Eng 2022; 19. [PMID: 36541542 DOI: 10.1088/1741-2552/aca82d] [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: 04/28/2022] [Accepted: 12/01/2022] [Indexed: 12/04/2022]
Abstract
Objective.The brain-computer interface (BCI) system based on sensorimotor rhythm can convert the human spirit into instructions for machine control, and it is a new human-computer interaction system with broad applications. However, the spatial resolution of scalp electroencephalogram (EEG) is limited due to the presence of volume conduction effects. Therefore, it is very meaningful to explore intracranial activities in a noninvasive way and improve the spatial resolution of EEG. Meanwhile, low-delay decoding is an essential factor for the development of a real-time BCI system.Approach.In this paper, EEG conduction is modeled by using public head anatomical templates, and cortical EEG is obtained using dynamic parameter statistical mapping. To solve the problem of a large amount of computation caused by the increase in the number of channels, the filter bank common spatial pattern method is used to obtain a spatial filter kernel, which reduces the computational cost of feature extraction to a linear level. And the feature classification and selection of important features are completed using a neural network containing band-spatial-time domain self-attention mechanisms.Main results.The results show that the method proposed in this paper achieves high accuracy for the four types of motor imagery EEG classification tasks, with fairly low latency and high physiological interpretability.Significance.The proposed decoding framework facilitates the realization of low-latency human-computer interaction systems.
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Affiliation(s)
- Tao Fang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Junkongshuai Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Wei Mu
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Zuoting Song
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Xueze Zhang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Gege Zhan
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Pengchao Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Jianxiong Bin
- Ji Hua Laboratory, Foshan, People's Republic of China
| | - Lan Niu
- Ji Hua Laboratory, Foshan, People's Republic of China
| | - Lihua Zhang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China.,Ji Hua Laboratory, Foshan, People's Republic of China
| | - Xiaoyang Kang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China.,Ji Hua Laboratory, Foshan, People's Republic of China.,Yiwu Research Institute of Fudan University, Yiwu City, People's Republic of China.,Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou, People's Republic of China.,Greater Bay Area Institute of Precision Medicine, Guangzhou, People's Republic of China
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20
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Syntax through the looking glass: A review on two-word linguistic processing across behavioral, neuroimaging and neurostimulation studies. Neurosci Biobehav Rev 2022; 142:104881. [DOI: 10.1016/j.neubiorev.2022.104881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 11/23/2022]
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21
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Croce P, Tecchio F, Tamburro G, Fiedler P, Comani S, Zappasodi F. Brain electrical microstate features as biomarkers of a stable motor output. J Neural Eng 2022; 19. [PMID: 36195069 DOI: 10.1088/1741-2552/ac975b] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 10/04/2022] [Indexed: 01/27/2023]
Abstract
Objective.The aim of the present study was to elucidate the brain dynamics underlying the maintenance of a constant force level exerted during a visually guided isometric contraction task by optimizing a predictive multivariate model based on global and spectral brain dynamics features.Approach.Electroencephalography (EEG) was acquired in 18 subjects who were asked to press a bulb and maintain a constant force level, indicated by a bar on a screen. For intervals of 500 ms, we calculated an index of force stability as well as indices of brain dynamics: microstate metrics (duration, occurrence, global explained variance, directional predominance) and EEG spectral amplitudes in the theta, low alpha, high alpha and beta bands. We optimized a multivariate regression model (partial least square (PLS)) where the microstate features and the spectral amplitudes were the input variables and the indexes of force stability were the output variables. The issues related to the collinearity among the input variables and to the generalizability of the model were addressed using PLS in a nested cross-validation approach.Main results.The optimized PLS regression model reached a good generalizability and succeeded to show the predictive value of microstates and spectral features in inferring the stability of the exerted force. Longer duration and higher occurrence of microstates, associated with visual and executive control networks, corresponded to better contraction performances, in agreement with the role played by the visual system and executive control network for visuo-motor integration.Significance.A combination of microstate metrics and brain rhythm amplitudes could be considered as biomarkers of a stable visually guided motor output not only at a group level, but also at an individual level. Our results may play an important role for a better understanding of the motor control in single trials or in real-time applications as well as in the study of motor control.
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Affiliation(s)
- Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, University 'Gabriele d'Annunzio' of Chieti-Pescara, Chieti, Italy.,Behavioral Imaging and Neural Dynamics Center, University 'Gabriele d'Annunzio' of Chieti-Pescara, Chieti, Italy
| | - Franca Tecchio
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), ISTC-CNR, Rome, Italy.,Fondazione Policlinico Gemelli IRCCS, Rome, Italy
| | - Gabriella Tamburro
- Department of Neuroscience, Imaging and Clinical Sciences, University 'Gabriele d'Annunzio' of Chieti-Pescara, Chieti, Italy.,Behavioral Imaging and Neural Dynamics Center, University 'Gabriele d'Annunzio' of Chieti-Pescara, Chieti, Italy
| | - Patrique Fiedler
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693 Ilmenau, Germany
| | - Silvia Comani
- Department of Neuroscience, Imaging and Clinical Sciences, University 'Gabriele d'Annunzio' of Chieti-Pescara, Chieti, Italy.,Behavioral Imaging and Neural Dynamics Center, University 'Gabriele d'Annunzio' of Chieti-Pescara, Chieti, Italy
| | - Filippo Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences, University 'Gabriele d'Annunzio' of Chieti-Pescara, Chieti, Italy.,Behavioral Imaging and Neural Dynamics Center, University 'Gabriele d'Annunzio' of Chieti-Pescara, Chieti, Italy.,Institute for Advanced Biomedical Technologies, University 'Gabriele d'Annunzio' of Chieti-Pescara, Chieti, Italy
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22
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Nakamura T, Dinh TH, Asai M, Nishimaru H, Matsumoto J, Setogawa T, Ichijo H, Honda S, Yamada H, Mihara T, Nishijo H. Characteristics of auditory steady-state responses to different click frequencies in awake intact macaques. BMC Neurosci 2022; 23:57. [PMID: 36180823 PMCID: PMC9524006 DOI: 10.1186/s12868-022-00741-9] [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: 05/26/2022] [Accepted: 09/13/2022] [Indexed: 11/28/2022] Open
Abstract
Background Auditory steady-state responses (ASSRs) are periodic evoked responses to constant periodic auditory stimuli, such as click trains, and are suggested to be associated with higher cognitive functions in humans. Since ASSRs are disturbed in human psychiatric disorders, recording ASSRs from awake intact macaques would be beneficial to translational research as well as an understanding of human brain function and its pathology. However, ASSR has not been reported in awake macaques. Results Electroencephalograms (EEGs) were recorded from awake intact macaques, while click trains at 20–83.3 Hz were binaurally presented. EEGs were quantified based on event-related spectral perturbation (ERSP) and inter-trial coherence (ITC), and ASSRs were significantly demonstrated in terms of ERSP and ITC in awake intact macaques. A comparison of ASSRs among different click train frequencies indicated that ASSRs were maximal at 83.3 Hz. Furthermore, analyses of laterality indices of ASSRs showed that no laterality dominance of ASSRs was observed. Conclusions The present results demonstrated ASSRs, comparable to those in humans, in awake intact macaques. However, there were some differences in ASSRs between macaques and humans: macaques showed maximal ASSR responses to click frequencies higher than 40 Hz that has been reported to elicit maximal responses in humans, and showed no dominant laterality of ASSRs under the electrode montage in this study compared with humans with right hemisphere dominance. The future ASSR studies using awake intact macaques should be aware of these differences, and possible factors, to which these differences were ascribed, are discussed. Supplementary Information The online version contains supplementary material available at 10.1186/s12868-022-00741-9.
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Affiliation(s)
- Tomoya Nakamura
- System Emotional Science, Faculty of Medicine, University of Toyama, Sugitani2630, Toyama, 930-0194, Japan.,Department of Anatomy, Faculty of Medicine, University of Toyama, Toyama, 930-0194, Japan
| | - Trong Ha Dinh
- System Emotional Science, Faculty of Medicine, University of Toyama, Sugitani2630, Toyama, 930-0194, Japan.,Department of Physiology, Vietnam Military Medical University, Hanoi, 100000, Vietnam
| | - Makoto Asai
- Candidate Discovery Science Labs, Drug Discovery Research, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Hiroshi Nishimaru
- System Emotional Science, Faculty of Medicine, University of Toyama, Sugitani2630, Toyama, 930-0194, Japan.,Research Center for Idling Brain Science (RCIBS), University of Toyama, Toyama, 930-0194, Japan
| | - Jumpei Matsumoto
- System Emotional Science, Faculty of Medicine, University of Toyama, Sugitani2630, Toyama, 930-0194, Japan.,Research Center for Idling Brain Science (RCIBS), University of Toyama, Toyama, 930-0194, Japan
| | - Tsuyoshi Setogawa
- System Emotional Science, Faculty of Medicine, University of Toyama, Sugitani2630, Toyama, 930-0194, Japan.,Research Center for Idling Brain Science (RCIBS), University of Toyama, Toyama, 930-0194, Japan
| | - Hiroyuki Ichijo
- Department of Anatomy, Faculty of Medicine, University of Toyama, Toyama, 930-0194, Japan
| | - Sokichi Honda
- Candidate Discovery Science Labs, Drug Discovery Research, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Hiroshi Yamada
- Candidate Discovery Science Labs, Drug Discovery Research, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Takuma Mihara
- Candidate Discovery Science Labs, Drug Discovery Research, Astellas Pharma Inc., Tsukuba, Ibaraki, 305-8585, Japan
| | - Hisao Nishijo
- System Emotional Science, Faculty of Medicine, University of Toyama, Sugitani2630, Toyama, 930-0194, Japan. .,Research Center for Idling Brain Science (RCIBS), University of Toyama, Toyama, 930-0194, Japan.
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23
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Qin Y, Hu Z, Chen Y, Liu J, Jiang L, Che Y, Han C. Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1093. [PMID: 36010760 PMCID: PMC9407608 DOI: 10.3390/e24081093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/06/2022] [Accepted: 08/06/2022] [Indexed: 06/15/2023]
Abstract
Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver's attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain's information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain's local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue.
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24
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El-Fiqi H, Wang M, Kasmarik K, Bezerianos A, Tan KC, Abbass HA. Weighted Gate Layer Autoencoders. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:7242-7253. [PMID: 33502995 DOI: 10.1109/tcyb.2021.3049583] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
A single dataset could hide a significant number of relationships among its feature set. Learning these relationships simultaneously avoids the time complexity associated with running the learning algorithm for every possible relationship, and affords the learner with an ability to recover missing data and substitute erroneous ones by using available data. In our previous research, we introduced the gate-layer autoencoders (GLAEs), which offer an architecture that enables a single model to approximate multiple relationships simultaneously. GLAE controls what an autoencoder learns in a time series by switching on and off certain input gates, thus, allowing and disallowing the data to flow through the network to increase network's robustness. However, GLAE is limited to binary gates. In this article, we generalize the architecture to weighted gate layer autoencoders (WGLAE) through the addition of a weight layer to update the error according to which variables are more critical and to encourage the network to learn these variables. This new weight layer can also be used as an output gate and uses additional control parameters to afford the network with abilities to represent different models that can learn through gating the inputs. We compare the architecture against similar architectures in the literature and demonstrate that the proposed architecture produces more robust autoencoders with the ability to reconstruct both incomplete synthetic and real data with high accuracy.
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25
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Plucińska R, Jędrzejewski K, Waligóra M, Malinowska U, Rogala J. Impact of EEG Frequency Bands and Data Separation on the Performance of Person Verification Employing Neural Networks. SENSORS (BASEL, SWITZERLAND) 2022; 22:5529. [PMID: 35898033 PMCID: PMC9332713 DOI: 10.3390/s22155529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/05/2022] [Accepted: 07/22/2022] [Indexed: 06/15/2023]
Abstract
The paper is devoted to the study of EEG-based people verification. Analyzed solutions employed shallow artificial neural networks using spectral EEG features as input representation. We investigated the impact of the features derived from different frequency bands and their combination on verification results. Moreover, we studied the influence of a number of hidden neurons in a neural network. The datasets used in the analysis consisted of signals recorded during resting state from 29 healthy adult participants performed on different days, 20 EEG sessions for each of the participants. We presented two different scenarios of training and testing processes. In the first scenario, we used different parts of each recording session to create the training and testing datasets, and in the second one, training and testing datasets originated from different recording sessions. Among single frequency bands, the best outcomes were obtained for the beta frequency band (mean accuracy of 91 and 89% for the first and second scenarios, respectively). Adding the spectral features from more frequency bands to the beta band features improved results (95.7 and 93.1%). The findings showed that there is not enough evidence that the results are different between networks using different numbers of hidden neurons. Additionally, we included results for the attack of 23 external impostors whose recordings were not used earlier in training or testing the neural network in both scenarios. Another significant finding of our study shows worse sensitivity results in the second scenario. This outcome indicates that most of the studies presenting verification or identification results based on the first scenario (dominating in the current literature) are overestimated when it comes to practical applications.
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Affiliation(s)
- Renata Plucińska
- Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland;
| | - Konrad Jędrzejewski
- Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland;
| | - Marek Waligóra
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology, 02-093 Warsaw, Poland; (M.W.); (U.M.)
| | - Urszula Malinowska
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology, 02-093 Warsaw, Poland; (M.W.); (U.M.)
| | - Jacek Rogala
- Institute of Physiology and Pathology of Hearing, Bioimaging Research Center, World Hearing Center, Kajetany, 05-830 Nadarzyn, Poland;
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26
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Shou G, Yuan H, Cha YH, Sweeney JA, Ding L. Age-related changes of whole-brain dynamics in spontaneous neuronal coactivations. Sci Rep 2022; 12:12140. [PMID: 35840643 PMCID: PMC9287374 DOI: 10.1038/s41598-022-16125-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/05/2022] [Indexed: 01/04/2023] Open
Abstract
Human brains experience whole-brain anatomic and functional changes throughout the lifespan. Age-related whole-brain network changes have been studied with functional magnetic resonance imaging (fMRI) to determine their low-frequency spatial and temporal characteristics. However, little is known about age-related changes in whole-brain fast dynamics at the scale of neuronal events. The present study investigated age-related whole-brain dynamics in resting-state electroencephalography (EEG) signals from 73 healthy participants from 6 to 65 years old via characterizing transient neuronal coactivations at a resolution of tens of milliseconds. These uncovered transient patterns suggest fluctuating brain states at different energy levels of global activations. Our results indicate that with increasing age, shorter lifetimes and more occurrences were observed in the brain states that show the global high activations and more consecutive visits to the global highest-activation brain state. There were also reduced transitional steps during consecutive visits to the global lowest-activation brain state. These age-related effects suggest reduced stability and increased fluctuations when visiting high-energy brain states and with a bias toward staying low-energy brain states. These age-related whole-brain dynamics changes are further supported by changes observed in classic alpha and beta power, suggesting its promising applications in examining the effect of normal healthy brain aging, brain development, and brain disease.
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Affiliation(s)
- Guofa Shou
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, USA
| | - Han Yuan
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, USA.,Institute for Biomedical Engineering, Science, and Technology, University of Oklahoma, Norman, USA
| | - Yoon-Hee Cha
- Department of Neurology, University of Minnesota, Minneapolis, MN, USA
| | - John A Sweeney
- Department of Psychiatry, University of Cincinnati, Cincinnati, OH, USA
| | - Lei Ding
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, USA. .,Institute for Biomedical Engineering, Science, and Technology, University of Oklahoma, Norman, USA. .,University of Oklahoma, 173 Felgar St., Gallogly Hall, Room 101, Norman, OK, 73019, USA.
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27
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Chen J, Min C, Wang C, Tang Z, Liu Y, Hu X. Electroencephalograph-Based Emotion Recognition Using Brain Connectivity Feature and Domain Adaptive Residual Convolution Model. Front Neurosci 2022; 16:878146. [PMID: 35812226 PMCID: PMC9257260 DOI: 10.3389/fnins.2022.878146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
In electroencephalograph (EEG) emotion recognition research, obtaining high-level emotional features with more discriminative information has become the key to improving the classification performance. This study proposes a new end-to-end emotion recognition method based on brain connectivity (BC) features and domain adaptive residual convolutional network (short for BC-DA-RCNN), which could effectively extract the spatial connectivity information related to the emotional state of the human brain and introduce domain adaptation to achieve accurate emotion recognition within and across the subject’s EEG signals. The BC information is represented by the global brain network connectivity matrix. The DA-RCNN is used to extract high-level emotional features between different dimensions of EEG signals, reduce the domain offset between different subjects, and strengthen the common features between different subjects. The experimental results on the large public DEAP data set show that the accuracy of the subject-dependent and subject-independent binary emotion classification in valence reaches 95.15 and 88.28%, respectively, which outperforms all the benchmark methods. The proposed method is proven to have lower complexity, better generalization ability, and domain robustness that help to lay a solid foundation for the development of high-performance affective brain-computer interface applications.
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28
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Gao J, Min X, Kang Q, Si H, Zhan H, Manyande A, Tian X, Dong Y, Zheng H, Song J. Effective connectivity in cortical networks during deception: A lie detection study using EEG. IEEE J Biomed Health Inform 2022; 26:3755-3766. [PMID: 35522638 DOI: 10.1109/jbhi.2022.3172994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Previous studies have identified activated regions associated with deceptive tasks and most of them utilized time, frequency, or temporal features to identify deceptive responses. However, when deception behaviors occur, the functional connectivity pattern and the communication between different brain areas remain largely unclear. In this study, we explored the most important information flows between different brain cortices during deception. First, we employed the guilty knowledge test protocol and recorded on 64 electrodes electroencephalogram (EEG) signals from 30 subjects (15 guilty and 15 innocent). EEG source estimation was then performed to compute the cortical activities on the 24 regions of interest (ROIs). Next, effective connectivity was calculated by partial directed coherence (PDC) analysis applied to the cortical signals. Furthermore, based on the graph-theoretical analysis, the network parameters with significant differences were extracted as features to identify two groups of subjects. In addition, the ROIs frequently involved in the above network parameters were selected, and based on the difference in the group mean of PDC values of all the edges connected with the selected ROIs, we presented the strongest information flows (MIIF) in the guilty group relative to the innocent group. Experimental results first show that the optimal classification features are mainly in-degree and out-degree measures of the ROI and the high classification accuracy for four bands demonstrated that the proposed method is suitable for lie detection. In addition, the frontoparietal network was found to be most prominent among all the MIIFs in four bands. Finally, combining the neurophysiology signification of four frequency bands, respectively, we analyzed the roles of all the important information flows to uncover the underlying cognitive processes and mechanisms used in deception.
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29
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Shim M, Im CH, Lee SH, Hwang HJ. Enhanced Performance by Interpretable Low-Frequency Electroencephalogram Oscillations in the Machine Learning-Based Diagnosis of Post-traumatic Stress Disorder. Front Neuroinform 2022; 16:811756. [PMID: 35571868 PMCID: PMC9094422 DOI: 10.3389/fninf.2022.811756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Electroencephalography (EEG)-based diagnosis of psychiatric diseases using machine-learning approaches has made possible the objective diagnosis of various psychiatric diseases. The objective of this study was to improve the performance of a resting-state EEG-based computer-aided diagnosis (CAD) system to diagnose post-traumatic stress disorder (PTSD), by optimizing the frequency bands used to extract EEG features. We used eyes-closed resting-state EEG data recorded from 77 PTSD patients and 58 healthy controls (HC). Source-level power spectrum densities (PSDs) of the resting-state EEG data were extracted from 6 frequency bands (delta, theta, alpha, low-beta, high-beta, and gamma), and the PSD features of each frequency band and their combinations were independently used to discriminate PTSD and HC. The classification performance was evaluated using support vector machine with leave-one-out cross validation. The PSD features extracted from slower-frequency bands (delta and theta) showed significantly higher classification performance than those of relatively higher-frequency bands. The best classification performance was achieved when using delta PSD features (86.61%), which was significantly higher than that reported in a recent study by about 13%. The PSD features selected to obtain better classification performances could be explained from a neurophysiological point of view, demonstrating the promising potential to develop a clinically reliable EEG-based CAD system for PTSD diagnosis.
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Affiliation(s)
- Miseon Shim
- Department of Electronics and Information, Korea University, Sejong, South Korea
- Industry Development Institute, Korea University, Sejong, South Korea
| | - Chang-Hwan Im
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Seung-Hwan Lee
- Department of Psychiatry, Ilsan Paik Hospital, Inje University, Goyang, South Korea
- Clinical Emotion and Cognition Research Laboratory, Goyang, South Korea
| | - Han-Jeong Hwang
- Department of Electronics and Information, Korea University, Sejong, South Korea
- Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong, South Korea
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30
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Alteration of cortical functional networks in mood disorders with resting-state electroencephalography. Sci Rep 2022; 12:5920. [PMID: 35396563 PMCID: PMC8993886 DOI: 10.1038/s41598-022-10038-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/24/2022] [Indexed: 01/10/2023] Open
Abstract
Studies comparing bipolar disorder (BD) and major depressive disorder (MDD) are scarce, and the neuropathology of these disorders is poorly understood. This study investigated source-level cortical functional networks using resting-state electroencephalography (EEG) in patients with BD and MDD. EEG was recorded in 35 patients with BD, 39 patients with MDD, and 42 healthy controls (HCs). Graph theory-based source-level weighted functional networks were assessed via strength, clustering coefficient (CC), and path length (PL) in six frequency bands. At the global level, patients with BD and MDD showed higher strength and CC, and lower PL in the high beta band, compared to HCs. At the nodal level, compared to HCs, patients with BD showed higher high beta band nodal CCs in the right precuneus, left isthmus cingulate, bilateral paracentral, and left superior frontal; however, patients with MDD showed higher nodal CC only in the right precuneus compared to HCs. Although both MDD and BD patients had similar global level network changes, they had different nodal level network changes compared to HCs. Our findings might suggest more altered cortical functional network in patients with BD than in those with MDD.
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31
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Yeung MK, Chu VW. Viewing neurovascular coupling through the lens of combined EEG-fNIRS: A systematic review of current methods. Psychophysiology 2022; 59:e14054. [PMID: 35357703 DOI: 10.1111/psyp.14054] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/01/2022] [Accepted: 03/08/2022] [Indexed: 12/25/2022]
Abstract
Neurovascular coupling is a key physiological mechanism that occurs in the healthy human brain, and understanding this process has implications for understanding the aging and neuropsychiatric populations. Combined electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) has emerged as a promising, noninvasive tool for probing neurovascular interactions in humans. However, the utility of this approach critically depends on the methodological quality used for multimodal integration. Despite a growing number of combined EEG-fNIRS applications reported in recent years, the methodological rigor of past studies remains unclear, limiting the accurate interpretation of reported findings and hindering the translational application of this multimodal approach. To fill this knowledge gap, we critically evaluated various methodological aspects of previous combined EEG-fNIRS studies performed in healthy individuals. A literature search was conducted using PubMed and PsycINFO on June 28, 2021. Studies involving concurrent EEG and fNIRS measurements in awake and healthy individuals were selected. After screening and eligibility assessment, 96 studies were included in the methodological evaluation. Specifically, we critically reviewed various aspects of participant sampling, experimental design, signal acquisition, data preprocessing, outcome selection, data analysis, and results presentation reported in these studies. Altogether, we identified several notable strengths and limitations of the existing EEG-fNIRS literature. In light of these limitations and the features of combined EEG-fNIRS, recommendations are made to improve and standardize research practices to facilitate the use of combined EEG-fNIRS when studying healthy neurovascular coupling processes and alterations in neurovascular coupling among various populations.
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Affiliation(s)
- Michael K Yeung
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
| | - Vivian W Chu
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
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32
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Croce P, Ricci L, Pulitano P, Boscarino M, Zappasodi F, Lanzone J, Narducci F, Mecarelli O, Di Lazzaro V, Tombini M, Assenza G. Machine learning for predicting levetiracetam treatment response in temporal lobe epilepsy. Clin Neurophysiol 2021; 132:3035-3042. [PMID: 34717224 DOI: 10.1016/j.clinph.2021.08.024] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 07/28/2021] [Accepted: 08/29/2021] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To determine the predictive power for seizure-freedom of 19-channels EEG, measured both before and after three months the initiation of the use of Levetiracetam (LEV), in a cohort of people after a new diagnosis of temporal-lobe epilepsy (TLE) using a machine-learning approach. METHODS Twenty-three individuals with TLE were examined. We dichotomized clinical outcome into seizure-free (SF) and non-seizure-free (NSF) after two years of LEV. EEG effective power in different frequency bands was compared using baseline EEG (T0) and the EEG after three months of LEV therapy (T1) between SF and NSF patients. Partial Least Square (PLS) analysis was used to test and validate the prediction of the model for clinical outcome. RESULTS A total of 152 features were extracted from the EEG recordings. When considering only the features calculated at T1, a predictive power for seizure-freedom (AUC = 0.750) was obtained. When employing both T0 and T1 features, an AUC = 0.800 was obtained. CONCLUSIONS This study provides a proof-of-concept pipeline for predicting the clinical response to anti-seizure medications in people with epilepsy. SIGNIFICANCE Future studies may benefit from the pipeline proposed in this study in order to develop a model that can match each patient to the most effective anti-seizure medication.
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Affiliation(s)
- Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Lorenzo Ricci
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, University Campus Bio-Medico of Rome, via Álvaro del Portillo, 21, 00128, Rome, Italy.
| | - Patrizia Pulitano
- Department of Neurology and Psychiatry, "Sapienza" University of Rome, Italy
| | - Marilisa Boscarino
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, University Campus Bio-Medico of Rome, via Álvaro del Portillo, 21, 00128, Rome, Italy
| | - Filippo Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies (ITAB), G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Jacopo Lanzone
- Department of Systems Medicine, Neuroscience, University of Rome Tor Vergata, Rome, Italy; Neurorehabilitation Department, IRCCS Salvatore Maugeri Foundation, Institute of Milan, Milan, Italy
| | - Flavia Narducci
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, University Campus Bio-Medico of Rome, via Álvaro del Portillo, 21, 00128, Rome, Italy
| | - Oriano Mecarelli
- Department of Neurology and Psychiatry, "Sapienza" University of Rome, Italy
| | - Vincenzo Di Lazzaro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, University Campus Bio-Medico of Rome, via Álvaro del Portillo, 21, 00128, Rome, Italy
| | - Mario Tombini
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, University Campus Bio-Medico of Rome, via Álvaro del Portillo, 21, 00128, Rome, Italy
| | - Giovanni Assenza
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, University Campus Bio-Medico of Rome, via Álvaro del Portillo, 21, 00128, Rome, Italy
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33
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Fatigue Detection with Spatial-Temporal Fusion Method on Covariance Manifolds of Electroencephalography. ENTROPY 2021; 23:e23101298. [PMID: 34682022 PMCID: PMC8534373 DOI: 10.3390/e23101298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 09/22/2021] [Accepted: 09/28/2021] [Indexed: 01/04/2023]
Abstract
With the increasing pressure of current life, fatigue caused by high-pressure work has deeply affected people and even threatened their lives. In particular, fatigue driving has become a leading cause of traffic accidents and deaths. This paper investigates electroencephalography (EEG)-based fatigue detection for driving by mining the latent information through the spatial-temporal changes in the relations between EEG channels. First, EEG data are partitioned into several segments to calculate the covariance matrices of each segment, and then we feed these matrices into a recurrent neural network to obtain high-level temporal information. Second, the covariance matrices of whole signals are leveraged to extract two kinds of spatial features, which will be fused with temporal characteristics to obtain comprehensive spatial-temporal information. Experiments on an open benchmark showed that our method achieved an excellent classification accuracy of 93.834% and performed better than several novel methods. These experimental results indicate that our method enables better reliability and feasibility in the detection of fatigued driving.
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34
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Finnie PSB, Komorowski RW, Bear MF. The spatiotemporal organization of experience dictates hippocampal involvement in primary visual cortical plasticity. Curr Biol 2021; 31:3996-4008.e6. [PMID: 34314678 PMCID: PMC8524775 DOI: 10.1016/j.cub.2021.06.079] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/26/2021] [Accepted: 06/25/2021] [Indexed: 11/18/2022]
Abstract
The hippocampus and neocortex are theorized to be crucial partners in the formation of long-term memories. Here, we assess hippocampal involvement in two related forms of experience-dependent plasticity in the primary visual cortex (V1) of mice. Like control animals, those with hippocampal lesions exhibit potentiation of visually evoked potentials after passive daily exposure to a phase-reversing oriented grating stimulus, which is accompanied by long-term habituation of a reflexive behavioral response. Thus, low-level recognition memory is formed independently of the hippocampus. However, response potentiation resulting from daily exposure to a fixed sequence of four oriented gratings is severely impaired in mice with hippocampal damage. A feature of sequence plasticity in V1 of controls, which is absent in lesioned mice, is the generation of predictive responses to an anticipated stimulus element when it is withheld or delayed. Thus, the hippocampus is involved in encoding temporally structured experience, even within the primary sensory cortex.
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Affiliation(s)
- Peter S B Finnie
- Massachusetts Institute of Technology, The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Robert W Komorowski
- Massachusetts Institute of Technology, The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Mark F Bear
- Massachusetts Institute of Technology, The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Avenue, Cambridge, MA 02139, USA.
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35
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Machine-learning-based diagnosis of drug-naive adult patients with attention-deficit hyperactivity disorder using mismatch negativity. Transl Psychiatry 2021; 11:484. [PMID: 34537812 PMCID: PMC8449778 DOI: 10.1038/s41398-021-01604-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 08/23/2021] [Accepted: 09/01/2021] [Indexed: 02/08/2023] Open
Abstract
Relatively little is investigated regarding the neurophysiology of adult attention-deficit/hyperactivity disorder (ADHD). Mismatch negativity (MMN) is an event-related potential component representing pre-attentive auditory processing, which is closely associated with cognitive status. We investigated MMN features as biomarkers to classify drug-naive adult patients with ADHD and healthy controls (HCs). Sensor-level features (amplitude and latency) and source-level features (source activation) of MMN were investigated and compared between the electroencephalograms of 34 patients with ADHD and 45 HCs using a passive auditory oddball paradigm. Correlations between MMN features and ADHD symptoms were analyzed. Finally, we applied machine learning to differentiate the two groups using sensor- and source-level features of MMN. Adult patients with ADHD showed significantly lower MMN amplitudes at the frontocentral electrodes and reduced MMN source activation in the frontal, temporal, and limbic lobes, which were closely associated with MMN generators and ADHD pathophysiology. Source activities were significantly correlated with ADHD symptoms. The best classification performance for adult ADHD patients and HCs showed an 81.01% accuracy, 82.35% sensitivity, and 80.00% specificity based on MMN source activity features. Our results suggest that abnormal MMN reflects the adult ADHD patients' pathophysiological characteristics and might serve clinically as a neuromarker of adult ADHD.
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36
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Li G, Jiang S, Paraskevopoulou SE, Chai G, Wei Z, Liu S, Wang M, Xu Y, Fan Z, Wu Z, Chen L, Zhang D, Zhu X. Detection of human white matter activation and evaluation of its function in movement decoding using stereo-electroencephalography (SEEG). J Neural Eng 2021; 18. [PMID: 34284361 DOI: 10.1088/1741-2552/ac160e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 07/20/2021] [Indexed: 11/11/2022]
Abstract
Objective. White matter tissue takes up approximately 50% of the human brain volume and it is widely known as a messenger conducting information between areas of the central nervous system. However, the characteristics of white matter neural activity and whether white matter neural recordings can contribute to movement decoding are often ignored and still remain largely unknown. In this work, we make quantitative analyses to investigate these two important questions using invasive neural recordings.Approach. We recorded stereo-electroencephalography (SEEG) data from 32 human subjects during a visually-cued motor task, where SEEG recordings can tap into gray and white matter electrical activity simultaneously. Using the proximal tissue density method, we identified the location (i.e. gray or white matter) of each SEEG contact. Focusing on alpha oscillatory and high gamma activities, we compared the activation patterns between gray matter and white matter. Then, we evaluated the performance of such white matter activation in movement decoding.Main results. The results show that white matter also presents activation under the task, in a similar way with the gray matter but at a significantly lower amplitude. Additionally, this work also demonstrates that combing white matter neural activities together with that of gray matter significantly promotes the movement decoding accuracy than using gray matter signals only.Significance. Taking advantage of SEEG recordings from a large number of subjects, we reveal the response characteristics of white matter neural signals under the task and demonstrate its enhancing function in movement decoding. This study highlights the importance of taking white matter activities into consideration in further scientific research and translational applications.
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Affiliation(s)
- Guangye Li
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China.,These authors contributed to this paper equally and should be considered as co-first authors
| | - Shize Jiang
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, People's Republic of China.,These authors contributed to this paper equally and should be considered as co-first authors
| | - Sivylla E Paraskevopoulou
- National Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of Health, Albany, NY, United States of America.,These authors contributed to this paper equally and should be considered as co-first authors
| | - Guohong Chai
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Zixuan Wei
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Shengjie Liu
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Meng Wang
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Yang Xu
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Zhen Fan
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Zehan Wu
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Liang Chen
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Dingguo Zhang
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Xiangyang Zhu
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
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37
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Ensemble multi-modal brain source localization using theory of evidence. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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38
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Carlos FLP, Ubirakitan MM, Rodrigues MCA, Aguilar-Domingo M, Herrera-Gutiérrez E, Gómez-Amor J, Copelli M, Carelli PV, Matias FS. Anticipated synchronization in human EEG data: Unidirectional causality with negative phase lag. Phys Rev E 2021; 102:032216. [PMID: 33075996 DOI: 10.1103/physreve.102.032216] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 07/15/2020] [Indexed: 11/07/2022]
Abstract
Understanding the functional connectivity of the brain has become a major goal of neuroscience. In many situations the relative phase difference, together with coherence patterns, has been employed to infer the direction of the information flow. However, it has been recently shown in local field potential data from monkeys the existence of a synchronized regime in which unidirectionally coupled areas can present both positive and negative phase differences. During the counterintuitive regime, called anticipated synchronization (AS), the phase difference does not reflect the causality. Here we investigate coherence and causality at the alpha frequency band (f∼10 Hz) between pairs of electroencephalogram (EEG) electrodes in humans during a GO/NO-GO task. We show that human EEG signals can exhibit anticipated synchronization, which is characterized by a unidirectional influence from an electrode A to an electrode B, but the electrode B leads the electrode A in time. To the best of our knowledge, this is the first verification of AS in EEG signals and in the human brain. The usual delayed synchronization (DS) regime is also present between many pairs. DS is characterized by a unidirectional influence from an electrode A to an electrode B and a positive phase difference between A and B which indicates that the electrode A leads the electrode B in time. Moreover we show that EEG signals exhibit diversity in the phase relations: the pairs of electrodes can present in-phase, antiphase, or out-of-phase synchronization with a similar distribution of positive and negative phase differences.
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Affiliation(s)
| | - Maciel-Monteiro Ubirakitan
- Grupo de Neurodinâmica, Departamento de Fisiologia e Farmacologia, Universidade Federal de Pernambuco, Recife PE 50670-901, Brazil.,Spanish Foundation for Neurometrics Development, Department of Psychophysics & Psychophysiology, 30100, Murcia, Spain
| | - Marcelo Cairrão Araújo Rodrigues
- Grupo de Neurodinâmica, Departamento de Fisiologia e Farmacologia, Universidade Federal de Pernambuco, Recife PE 50670-901, Brazil
| | - Moisés Aguilar-Domingo
- Spanish Foundation for Neurometrics Development, Department of Psychophysics & Psychophysiology, 30100, Murcia, Spain.,Department of Human Anatomy and Psychobiology, Faculty of Psychology, University of Murcia, 30100 Espinardo Campus, Murcia, Spain
| | - Eva Herrera-Gutiérrez
- Department of Developmental and Educational Psychology, Faculty of Psychology, University of Murcia, 30100 Espinardo Campus, Murcia, Spain
| | - Jesús Gómez-Amor
- Department of Human Anatomy and Psychobiology, Faculty of Psychology, University of Murcia, 30100 Espinardo Campus, Murcia, Spain
| | - Mauro Copelli
- Departamento de Física, Universidade Federal de Pernambuco, Recife PE 50670-901, Brazil
| | - Pedro V Carelli
- Departamento de Física, Universidade Federal de Pernambuco, Recife PE 50670-901, Brazil
| | - Fernanda S Matias
- Instituto de Física, Universidade Federal de Alagoas, Maceió, Alagoas 57072-970 Brazil
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39
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Effects of long-term unilateral cochlear implant use on large-scale network synchronization in adolescents. Hear Res 2021; 409:108308. [PMID: 34343851 DOI: 10.1016/j.heares.2021.108308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 06/25/2021] [Accepted: 06/29/2021] [Indexed: 11/20/2022]
Abstract
Unilateral cochlear implantation (CI) limits deafness-related changes in the auditory pathways but promotes abnormal cortical preference for the stimulated ear and leaves the opposite ear with little protection from auditory deprivation. In the present study, time-frequency analyses of event-related potentials elicited from stimuli presented to each ear were used to determine effects of unilateral CI use on cortical synchrony. CI-elicited activity in 34 adolescents (15.4±1.9 years of age) who had listened with unilateral CIs for most of their lives prior to bilateral implantation were compared to responses elicited by a 500Hz tone-burst in normal hearing peers. Phase-locking values between 4 and 60Hz were calculated for 171 pairs of 19-cephalic recording electrodes. Ear specific results were found in the normal hearing group: higher synchronization in low frequency bands (theta and alpha) from left ear stimulation in the right hemisphere and more high frequency activity (gamma band) from right ear stimulation in the left hemisphere. In the CI group, increased phase synchronization in the theta and beta frequencies with bursts of gamma activity were elicited by the experienced-right CI between frontal, temporal and parietal cortical regions in both hemispheres, consistent with increased recruitment of cortical areas involved in attention and higher-order processes, potentially to support unilateral listening. By contrast, activity was globally desynchronized in response to initial stimulation of the naïve-left ear, suggesting decoupling of these pathways from the cortical hearing network. These data reveal asymmetric auditory development promoted by unilateral CI use, resulting in an abnormally mature neural network.
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40
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Short MR, Hernandez-Pavon JC, Jones A, Pons JL. EEG hyperscanning in motor rehabilitation: a position paper. J Neuroeng Rehabil 2021; 18:98. [PMID: 34112208 PMCID: PMC8194127 DOI: 10.1186/s12984-021-00892-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 05/31/2021] [Indexed: 11/10/2022] Open
Abstract
Studying the human brain during interpersonal interaction allows us to answer many questions related to motor control and cognition. For instance, what happens in the brain when two people walking side by side begin to change their gait and match cadences? Adapted from the neuroimaging techniques used in single-brain measurements, hyperscanning (HS) is a technique used to measure brain activity from two or more individuals simultaneously. Thus far, HS has primarily focused on healthy participants during social interactions in order to characterize inter-brain dynamics. Here, we advocate for expanding the use of this electroencephalography hyperscanning (EEG-HS) technique to rehabilitation paradigms in individuals with neurological diagnoses, namely stroke, spinal cord injury (SCI), Parkinson's disease (PD), and traumatic brain injury (TBI). We claim that EEG-HS in patient populations with impaired motor function is particularly relevant and could provide additional insight on neural dynamics, optimizing rehabilitation strategies for each individual patient. In addition, we discuss future technologies related to EEG-HS that could be developed for use in the clinic as well as technical limitations to be considered in these proposed settings.
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Affiliation(s)
- Matthew R Short
- Legs + Walking Lab, Shirley Ryan AbilityLab, Floor 24, 355 E Erie St, Chicago, IL, 60611, USA.,Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Chicago, IL, USA
| | - Julio C Hernandez-Pavon
- Legs + Walking Lab, Shirley Ryan AbilityLab, Floor 24, 355 E Erie St, Chicago, IL, 60611, USA.,Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Alyssa Jones
- Legs + Walking Lab, Shirley Ryan AbilityLab, Floor 24, 355 E Erie St, Chicago, IL, 60611, USA
| | - Jose L Pons
- Legs + Walking Lab, Shirley Ryan AbilityLab, Floor 24, 355 E Erie St, Chicago, IL, 60611, USA. .,Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Chicago, IL, USA. .,Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
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41
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Courtney SM, Hinault T. When the time is right: Temporal dynamics of brain activity in healthy aging and dementia. Prog Neurobiol 2021; 203:102076. [PMID: 34015374 DOI: 10.1016/j.pneurobio.2021.102076] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 05/08/2021] [Accepted: 05/14/2021] [Indexed: 10/21/2022]
Abstract
Brain activity and communications are complex phenomena that dynamically unfold over time. However, in contrast with the large number of studies reporting neuroanatomical differences in activation relative to young adults, changes of temporal dynamics of neural activity during normal and pathological aging have been grossly understudied and are still poorly known. Here, we synthesize the current state of knowledge from MEG and EEG studies that aimed at specifying the effects of healthy and pathological aging on local and network dynamics, and discuss the clinical and theoretical implications of these findings. We argue that considering the temporal dynamics of brain activations and networks could provide a better understanding of changes associated with healthy aging, and the progression of neurodegenerative disease. Recent research has also begun to shed light on the association of these dynamics with other imaging modalities and with individual differences in cognitive performance. These insights hold great potential for driving new theoretical frameworks and development of biomarkers to aid in identifying and treating age-related cognitive changes.
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Affiliation(s)
- S M Courtney
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, 21218, USA; F.M. Kirby Research Center, Kennedy Krieger Institute, MD 21205, USA; Department of Neuroscience, Johns Hopkins University, MD 21205, USA
| | - T Hinault
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, 21218, USA; U1077 INSERM-EPHE-UNICAEN, Caen, France.
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42
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Philiastides MG, Tu T, Sajda P. Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI. Annu Rev Neurosci 2021; 44:315-334. [PMID: 33761268 DOI: 10.1146/annurev-neuro-100220-093239] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements.
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Affiliation(s)
- Marios G Philiastides
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8AD, Scotland;
| | - Tao Tu
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Paul Sajda
- Departments of Biomedical Engineering, Electrical Engineering, and Radiology and the Data Science Institute, Columbia University, New York, NY 10027, USA;
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43
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Kim S, Kim JS, Kwon YJ, Lee HY, Yoo JH, Lee YJ, Shim SH. Altered cortical functional network in drug-naive adult male patients with attention-deficit hyperactivity disorder: A resting-state electroencephalographic study. Prog Neuropsychopharmacol Biol Psychiatry 2021; 106:110056. [PMID: 32777325 DOI: 10.1016/j.pnpbp.2020.110056] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 07/08/2020] [Accepted: 08/03/2020] [Indexed: 02/03/2023]
Abstract
Relatively little is known about the neurophysiology of adult Attention-deficit/hyperactivity disorder (ADHD). Brain network analysis can yield important insights into the neuropathology in adult ADHD. The objective of this study was to investigate source-level cortical functional network using resting-state electroencephalography (EEG) in drug-naive adult patients with ADHD. Resting-state EEG was performed for 30 adult male patients with ADHD and 27 male healthy controls. Source-level weighted functional networks based on graph theory were evaluated, including strength, clustering coefficient (CC) and path length (PL) in six frequency bands. At the global level, strength (η2 = 0.167) and CC (η2 = 0.156) were lower while PL (η2 = 0.159) was higher for the high beta band in the ADHD patient group compared to healthy controls. At the nodal level, CCs of the high beta band were lower in the left middle temporal gyrus (η2 = 0.244), right inferior occipital cortex (η2 = 0.214), right posterior transverse collateral sulcus (η2 = 0.237), and right anterior occipital sulcus (η2 = 0.251) for the adult ADHD group. Furthermore, the nodal-level high beta band CCs of the left middle temporal gyrus and right anterior occipital sulcus were significantly negatively correlated with ADHD symptoms. The altered cortical functional network showed inefficient connectivity in the left middle temporal gyrus, belonging to the default mode network, the right inferior occipital cortex, belonging to the extrastriate visual resting state network, the right posterior transverse collateral sulcus, belonging to the visual network, and the anterior occipital sulcus, reflecting visual attention, which might affect the pathophysiology of ADHD. Taken together, these attenuated network inefficiencies in adult patients with ADHD may lead to suboptimal information processing and affect symptoms of ADHD, such as inattention and hyperactivity. Our findings should be further replicated using longitudinal study designs.
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Affiliation(s)
- Sungkean Kim
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
| | - Ji Sun Kim
- Department of Psychiatry, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Young Joon Kwon
- Department of Psychiatry, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Hwa Young Lee
- Department of Psychiatry, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Jae Hyun Yoo
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yeon Jung Lee
- Department of Psychiatry, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Republic of Korea
| | - Se-Hoon Shim
- Department of Psychiatry, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea.
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44
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Brauns K, Friedl-Werner A, Maggioni MA, Gunga HC, Stahn AC. Head-Down Tilt Position, but Not the Duration of Bed Rest Affects Resting State Electrocortical Activity. Front Physiol 2021; 12:638669. [PMID: 33716785 PMCID: PMC7951060 DOI: 10.3389/fphys.2021.638669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 01/28/2021] [Indexed: 11/13/2022] Open
Abstract
Adverse cognitive and behavioral conditions and psychiatric disorders are considered a critical and unmitigated risk during future long-duration space missions (LDSM). Monitoring and mitigating crew health and performance risks during these missions will require tools and technologies that allow to reliably assess cognitive performance and mental well-being. Electroencephalography (EEG) has the potential to meet the technical requirements for the non-invasive and objective monitoring of neurobehavioral conditions during LDSM. Weightlessness is associated with fluid and brain shifts, and these effects could potentially challenge the interpretation of resting state EEG recordings. Head-down tilt bed rest (HDBR) provides a unique spaceflight analog to study these effects on Earth. Here, we present data from two long-duration HDBR experiments, which were used to systematically investigate the time course of resting state electrocortical activity during prolonged HDBR. EEG spectral power significantly reduced within the delta, theta, alpha, and beta frequency bands. Likewise, EEG source localization revealed significantly lower activity in a broad range of centroparietal and occipital areas within the alpha and beta frequency domains. These changes were observed shortly after the onset of HDBR, did not change throughout HDBR, and returned to baseline after the cessation of bed rest. EEG resting state functional connectivity was not affected by HDBR. The results provide evidence for a postural effect on resting state brain activity that persists throughout long-duration HDBR, indicating that immobilization and inactivity per se do not affect resting state electrocortical activity during HDBR. Our findings raise an important issue on the validity of EEG to identify the time course of changes in brain function during prolonged HBDR, and highlight the importance to maintain a consistent body posture during all testing sessions, including data collections at baseline and recovery.
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Affiliation(s)
- Katharina Brauns
- Charité - Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Physiology, Berlin, Germany
| | - Anika Friedl-Werner
- Charité - Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Physiology, Berlin, Germany.,INSERM U 1075 COMETE, Université de Normandie, Caen, France
| | - Martina A Maggioni
- Charité - Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Physiology, Berlin, Germany.,Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
| | - Hanns-Christian Gunga
- Charité - Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Physiology, Berlin, Germany
| | - Alexander C Stahn
- Charité - Universitätsmedizin Berlin, a corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Physiology, Berlin, Germany.,Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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45
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Lew S, Hämäläinen MS, Ahlfors SP, Okada Y. Influence of unfused cranial bones on magnetoencephalography signals in human infants. Clin Neurophysiol 2020; 132:708-719. [PMID: 33571879 DOI: 10.1016/j.clinph.2020.11.036] [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: 03/12/2018] [Revised: 11/12/2020] [Accepted: 11/17/2020] [Indexed: 10/22/2022]
Abstract
OBJECTIVE To clarify the effects of unfused cranial bones on magnetoencephalography (MEG) signals during early development. METHODS In a simulation study, we compared the MEG signals over a spherical head model with a circular hole mimicking the anterior fontanel to those over the same head model without the fontanel for different head and fontanel sizes with varying skull thickness and conductivity. RESULTS The fontanel had small effects according to three indices. The sum of differences in signal over a sensor array due to a fontanel, for example, was < 6% of the sum without the fontanel. However, the fontanel effects were extensive for dipole sources deep in the brain or outside the fontanel for larger fontanels. The effects were comparable in magnitude for tangential and radial sources. Skull thickness significantly increased the effect, while skull conductivity had minor effects. CONCLUSION MEG signal is weakly affected by a fontanel. However, the effects can be extensive and significant for radial sources, thicker skull and large fontanels. The fontanel effects can be intuitively explained by the concept of secondary sources at the fontanel wall. SIGNIFICANCE The minor influence of unfused cranial bones simplifies MEG analysis, but it should be considered for quantitative analysis.
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Affiliation(s)
- Seok Lew
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Matti S Hämäläinen
- Harvard Medical School, Boston, MA 02115, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Seppo P Ahlfors
- Harvard Medical School, Boston, MA 02115, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Yoshio Okada
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA.
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46
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Emotional EEG classification using connectivity features and convolutional neural networks. Neural Netw 2020; 132:96-107. [DOI: 10.1016/j.neunet.2020.08.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 07/20/2020] [Accepted: 08/11/2020] [Indexed: 11/17/2022]
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47
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Chiarelli AM, Croce P, Assenza G, Merla A, Granata G, Giannantoni NM, Pizzella V, Tecchio F, Zappasodi F. Electroencephalography-Derived Prognosis of Functional Recovery in Acute Stroke Through Machine Learning Approaches. Int J Neural Syst 2020; 30:2050067. [PMID: 33236654 DOI: 10.1142/s0129065720500677] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Stroke, if not lethal, is a primary cause of disability. Early assessment of markers of recovery can allow personalized interventions; however, it is difficult to deliver indexes in the acute phase able to predict recovery. In this perspective, evaluation of electrical brain activity may provide useful information. A machine learning approach was explored here to predict post-stroke recovery relying on multi-channel electroencephalographic (EEG) recordings of few minutes performed at rest. A data-driven model, based on partial least square (PLS) regression, was trained on 19-channel EEG recordings performed within 10 days after mono-hemispheric stroke in 101 patients. The band-wise (delta: 1-4[Formula: see text]Hz, theta: 4-7[Formula: see text]Hz, alpha: 8-14[Formula: see text]Hz and beta: 15-30[Formula: see text]Hz) EEG effective powers were used as features to predict the recovery at 6 months (based on clinical status evaluated through the NIH Stroke Scale, NIHSS) in an optimized and cross-validated framework. In order to exploit the multimodal contribution to prognosis, the EEG-based prediction of recovery was combined with NIHSS scores in the acute phase and both were fed to a nonlinear support vector regressor (SVR). The prediction performance of EEG was at least as good as that of the acute clinical status scores. A posteriori evaluation of the features exploited by the analysis highlighted a lower delta and higher alpha activity in patients showing a positive outcome, independently of the affected hemisphere. The multimodal approach showed better prediction capabilities compared to the acute NIHSS scores alone ([Formula: see text] versus [Formula: see text], AUC = 0.80 versus AUC = 0.70, [Formula: see text]). The multimodal and multivariate model can be used in acute phase to infer recovery relying on standard EEG recordings of few minutes performed at rest together with clinical assessment, to be exploited for early and personalized therapies. The easiness of performing EEG may allow such an approach to become a standard-of-care and, thanks to the increasing number of labeled samples, further improving the model predictive power.
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Affiliation(s)
- Antonio Maria Chiarelli
- Department of Neuroscience, Imaging and Clinical Sciences and the Institute for Advanced Biomedical Technologies, Università G. d'Annunzio, Chieti, 66100, Italy
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences and the Institute for Advanced Biomedical Technologies, Università G. d'Annunzio, Chieti, 66100, Italy
| | - Giovanni Assenza
- Neurology, Neurophysiology and Neurobiology Unit, Department of Medicine, Campus Bio-Medico University of Rome, Rome, Italy
| | - Arcangelo Merla
- Department of Neuroscience, Imaging and Clinical Sciences and the Institute for Advanced Biomedical Technologies, Università G. d'Annunzio, Chieti, 66100, Italy
| | - Giuseppe Granata
- Fondazione Policlinico A. Gemelli IRCCS, Catholic University of Sacred Heart, Rome, Italy
| | | | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences and the Institute for Advanced Biomedical Technologies, Università G. d'Annunzio, Chieti, 66100, Italy
| | - Franca Tecchio
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Istituto di Scienze e Teconologie della Cognizione (ISTC) - Consiglio Nazionale delle Ricerche (CNR), Rome, Italy
| | - Filippo Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences and the Institute for Advanced Biomedical Technologies, Università G. d'Annunzio, Chieti, 66100, Italy
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Gonzalez-Astudillo J, Cattai T, Bassignana G, Corsi MC, De Vico Fallani F. Network-based brain computer interfaces: principles and applications. J Neural Eng 2020; 18. [PMID: 33147577 DOI: 10.1088/1741-2552/abc760] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 11/04/2020] [Indexed: 12/17/2022]
Abstract
Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user's mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability.
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Bowyer SM, Zillgitt A, Greenwald M, Lajiness-O'Neill R. Language Mapping With Magnetoencephalography: An Update on the Current State of Clinical Research and Practice With Considerations for Clinical Practice Guidelines. J Clin Neurophysiol 2020; 37:554-563. [DOI: 10.1097/wnp.0000000000000489] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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Kotiuchyi I, Pernice R, Popov A, Faes L, Kharytonov V. A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks. Brain Sci 2020; 10:E657. [PMID: 32971835 PMCID: PMC7564380 DOI: 10.3390/brainsci10090657] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 09/13/2020] [Accepted: 09/15/2020] [Indexed: 12/20/2022] Open
Abstract
This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at the level of electroencephalogram (EEG) sources. The framework combines the use of common spatial patterns to select the EEG components which maximize the variance between two experimental conditions, simultaneous implementation of vector autoregressive modeling (VAR) with independent component analysis to describe the joint source dynamics and their projection to the scalp, and computation of information dynamics measures (information storage, information transfer, statistically significant network links) from the source VAR parameters. The proposed framework was tested on simulated EEGs obtained mixing source signals generated under different coupling conditions, showing its ability to retrieve source information dynamics from the scalp signals. Then, it was applied to investigate scalp and source brain connectivity in a group of children manifesting episodes of focal and generalized epilepsy; the analysis was performed on EEG signals lasting 5 s, collected in two consecutive windows preceding and one window following each ictal episode. Our results show that generalized seizures are associated with a significant decrease from pre-ictal to post-ictal periods of the information stored in the signals and of the information transferred among them, reflecting reduced self-predictability and causal connectivity at the level of both scalp and source brain dynamics. On the contrary, in the case of focal seizures the scalp EEG activity was not discriminated across conditions by any information measure, while source analysis revealed a tendency of the measures of information transfer to increase just before seizures and to decrease just after seizures. These results suggest that focal epileptic seizures are associated with a reorganization of the topology of EEG brain networks which is only visible analyzing connectivity among the brain sources. Our findings emphasize the importance of EEG modeling approaches able to deal with the adverse effects of volume conduction on brain connectivity analysis, and their potential relevance to the development of strategies for prediction and clinical treatment of epilepsy.
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Affiliation(s)
- Ivan Kotiuchyi
- Department of Biomedical Engineering, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 03056 Kyiv, Ukraine;
- Data & Analytics, Ciklum, London WC1 A 2TH, UK;
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, 90133 Palermo, Italy;
| | - Anton Popov
- Data & Analytics, Ciklum, London WC1 A 2TH, UK;
- Department of Electronic Engineering, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 03056 Kyiv, Ukraine
| | - Luca Faes
- Department of Engineering, University of Palermo, 90133 Palermo, Italy;
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