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1.
Cogn Neurodyn ; 15(6): 939-948, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34790263

RESUMO

To promote the rehabilitation of motor function in children with cerebral palsy (CP), we developed motor imagery (MI) based training system to assist their motor rehabilitation. Eighteen CP children, ten in short- and eight in long-term rehabilitation, participated in our study. In short-term rehabilitation, every 2 days, the MI datasets were collected; whereas the duration of two adjacency MI experiments was ten days in the long-term protocol. Meanwhile, within two adjacency experiments, CP children were requested to daily rehabilitate the motor function based on our system for 30 min. In both strategies, the promoted motor information processing was observed. In terms of the relative signal power spectra, a main effect of time was revealed, as the promoted power spectra were found for the last time of MI recording, compared to that of the first one, which first validated the effectiveness of our intervention. Moreover, as for network efficiency related to the motor information processing, compared to the first MI, the increased network properties were found for the last MI, especially in long-term rehabilitation in which CP children experienced a more obvious efficiency promotion. These findings did validate that our MI-based rehabilitation system has the potential for CP children to assist their motor rehabilitation.

2.
Neuroimage ; 245: 118713, 2021 Nov 17.
Artigo em Inglês | MEDLINE | ID: mdl-34798231

RESUMO

The current evolution of 'cloud neuroscience' leads to more efforts with the large-scale EEG applications, by using EEG pipelines to handle the rapidly accumulating EEG data. However, there are a few specific cloud platforms that seek to address the cloud computational challenges of EEG big data analysis to benefit the EEG community. In response to the challenges, a WeBrain cloud platform (https://webrain.uestc.edu.cn/) is designed as a web-based brainformatics platform and computational ecosystem to enable large-scale EEG data storage, exploration and analysis using cloud high-performance computing (HPC) facilities. WeBrain connects researchers from different fields to EEG and multimodal tools that have become the norm in the field and the cloud processing power required to handle those large EEG datasets. This platform provides an easy-to-use system for novice users (even no computer programming skills) and provides satisfactory maintainability, sustainability and flexibility for IT administrators and tool developers. A range of resources are also available on https://webrain.uestc.edu.cn/, including documents, manuals, example datasets related to WeBrain, and collected links to open EEG datasets and tools. It is not necessary for users or administrators to install any software or system, and all that is needed is a modern web browser, which reduces the technical expertise required to use or manage WeBrain. The WeBrain platform is sponsored and driven by the China-Canada-Cuba international brain cooperation project (CCC-Axis, http://ccc-axis.org/), and we hope that WeBrain will be a promising cloud brainformatics platform for exploring brain information in large-scale EEG applications in the EEG community.

3.
Artigo em Inglês | MEDLINE | ID: mdl-34705652

RESUMO

Although the spatiotemporal complexity and network connectivity are clarified to be disrupted during the general anesthesia (GA) induced unconsciousness, it remains to be difficult to exactly monitor the fluctuation of consciousness clinically. In this study, to track the loss of consciousness (LOC) induced by GA, we first developed the multi-channel cross fuzzy entropy method to construct the time-varying networks, whose temporal fluctuations were then explored and quantitatively evaluated. Thereafter, an algorithm was further proposed to detect the time onset at which patients lost their consciousness. The results clarified during the resting state, relatively stable fuzzy fluctuations in multi-channel network architectures and properties were found; by contrast, during the LOC period, the disrupted frontal-occipital connectivity occurred at the early stage, while at the later stage, the inner-frontal connectivity was identified. When specifically exploring the early LOC stage, the uphill of the clustering coefficients and the downhill of the characteristic path length were found, which might help resolve the propofol-induced consciousness fluctuation in patients. Moreover, the developed detection algorithm was validated to have great capacity in exactly capturing the time point (in seconds) at which patients lost consciousness. The findings demonstrated that the time-varying cross-fuzzy networks help decode the GA and are of great significance for developing anesthesia depth monitoring technology clinically.


Assuntos
Estado de Consciência , Propofol , Anestesia Geral , Encéfalo , Eletroencefalografia , Entropia , Humanos , Inconsciência
4.
J Neural Eng ; 18(5)2021 09 30.
Artigo em Inglês | MEDLINE | ID: mdl-34534980

RESUMO

Objective.Unconsciousness is a key feature related to general anesthesia (GA) but is difficult to be evaluated accurately by anesthesiologists clinically.Approach.To tracking the loss of consciousness (LOC) and recovery of consciousness (ROC) under GA, in this study, by investigating functional connectivity of the scalp electroencephalogram, we explore any potential difference in brain networks among anesthesia induction, anesthesia recovery, and the resting state.Main results.The results of this study demonstrated significant differences among the three periods, concerning the corresponding brain networks. In detail, the suppressed default mode network, as well as the prolonged characteristic path length and decreased clustering coefficient, during LOC was found in the alpha band, compared to the Resting and the ROC state. When to further identify the Resting and LOC states, the fused network topologies and properties achieved the highest accuracy of 95%, along with a sensitivity of 93.33% and a specificity of 96.67%.Significance.The findings of this study not only deepen our understanding of propofol-induced unconsciousness but also provide quantitative measurements subserving better anesthesia management.


Assuntos
Estado de Consciência , Propofol , Anestesia Geral , Encéfalo , Humanos , Inconsciência/induzido quimicamente
5.
IEEE Trans Cybern ; PP2021 Aug 16.
Artigo em Inglês | MEDLINE | ID: mdl-34398778

RESUMO

As a kind of biological network, the brain network conduces to understanding the mystery of high-efficiency information processing in the brain, which will provide instructions to develop efficient brain-like neural networks. Large-scale dynamical functional network connectivity (dFNC) provides a more context-sensitive, dynamical, and straightforward sight at a higher network level. Nevertheless, dFNC analysis needs good enough resolution in both temporal and spatial domains, and the construction of dFNC needs to capture the time-varying correlations between two multivariate time series with unmatched spatial dimensions. Effective methods still lack. With well-developed source imaging techniques, electroencephalogram (EEG) has the potential to possess both high temporal and spatial resolutions. Therefore, we proposed to construct the EEG large-scale cortical dFNC based on brain atlas to probe the subtle dynamic activities in the brain and developed a novel method, that is, wavelet coherence-S estimator (WTCS), to assess the dynamic couplings among functional subnetworks with different spatial dimensions. The simulation study demonstrated its robustness and availability of applying to dFNC. The application in real EEG data revealed the appealing ``Primary peak'' and ``P3-like peak'' in dFNC network properties and meaningful evolutions in dFNC network topology for P300. Our study brings new insights for probing brain activities at a more dynamical and higher hierarchical level and pushing forward the development of brain-inspired artificial neural networks. The proposed WTCS not only benefits the dFNC studies but also gives a new solution to capture the time-varying couplings between the multivariate time series that is often encountered in signal processing disciplines.

7.
Mol Plant ; 14(11): 1814-1830, 2021 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-34242849

RESUMO

Post-translational modifications (PTMs), including phosphorylation and persulfidation, regulate the activity of SNF1-RELATED PROTEIN KINASE2.6 (SnRK2.6). Here, we report how persulfidations and phosphorylations of SnRK2.6 influence each other. The persulfidation of cysteine C131/C137 alters SnRK2.6 structure and brings the serine S175 residue closer to the aspartic acid D140 that acts as ATP-γ-phosphate proton acceptor, thereby improving the transfer efficiency of phosphate groups to S175 to enhance the phosphorylation level of S175. Interestingly, we predicted that S267 and C137 were predicted to lie in close proximity on the protein surface and found that the phosphorylation status of S267 positively regulates the persulfidation level at C137. Analyses of the responses of dephosphorylated and depersulfidated mutants to abscisic acid and the H2S-donor NaHS during stomatal closure, water loss, gas exchange, Ca2+ influx, and drought stress revealed that S175/S267-associated phosphorylation and C131/137-associated persulfidation are essential for SnRK2.6 function in vivo. In light of these findings, we propose a mechanistic model in which certain phosphorylations facilitate persulfidation, thereby changing the structure of SnRK2.6 and increasing its activity.

8.
J Neural Eng ; 18(4)2021 07 05.
Artigo em Inglês | MEDLINE | ID: mdl-34153948

RESUMO

Objective.Exploring the temporal variability in spatial topology during the resting state attracts growing interest and becomes increasingly useful to tackle the cognitive process of brain networks. In particular, the temporal brain dynamics during the resting state may be delineated and quantified aligning with cognitive performance, but few studies investigated the temporal variability in the electroencephalogram (EEG) network as well as its relationship with cognitive performance.Approach.In this study, we proposed an EEG-based protocol to measure the nonlinear complexity of the dynamic resting-state network by applying the fuzzy entropy. To further validate its applicability, the fuzzy entropy was applied into simulated and two independent datasets (i.e. decision-making and P300).Main results.The simulation study first proved that compared to the existing methods, this approach could not only exactly capture the pattern dynamics in time series but also overcame the magnitude effect of time series. Concerning the two EEG datasets, the flexible and robust network architectures of the brain cortex at rest were identified and distributed at the bilateral temporal lobe and frontal/occipital lobe, respectively, whose variability metrics were found to accurately classify different groups. Moreover, the temporal variability of resting-state network property was also either positively or negatively related to individual cognitive performance.Significance.This outcome suggested the potential of fuzzy entropy for evaluating the temporal variability of the dynamic resting-state brain networks, and the fuzzy entropy is also helpful for uncovering the fluctuating network variability that accounts for the individual decision differences.


Assuntos
Eletroencefalografia , Couro Cabeludo , Encéfalo , Córtex Cerebral , Entropia
9.
Int J Neural Syst ; 31(7): 2150031, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-34167448

RESUMO

Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems.


Assuntos
Tomada de Decisões , Jogo de Azar , Eletroencefalografia , Retroalimentação , Lobo Frontal , Humanos
10.
Brain Topogr ; 34(4): 403-414, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33950323

RESUMO

"Bad channels" are common phenomena during scalp electroencephalography (EEG) recording that arise due to various technique-related reasons, and reconstructing signals from bad channels is an inevitable choice in EEG processing. However, current interpolation methods are all based on purely mathematical interpolation theory, ignoring the neurophysiological basis of the EEG signals, and their performance needs to be further improved, especially when there are many scattered or adjacent bad channels. Therefore, a new interpolation method, named the reference electrode standardization interpolation technique (RESIT), was developed for interpolating scalp EEG channels. Resting-state and event-related EEG datasets were used to investigate the performance of the RESIT. The main results showed that (1) assuming 10% bad channels, RESIT can reconstruct the bad channels well; (2) as the percentage of bad channels increased (from 2% to 85%), the absolute and relative errors between the true and RESIT-reconstructed signals generally increased, and the correlations between the true and RESIT signals decreased; (3) for a range of bad channel percentages (2% ~ 85%), the RESIT had lower absolute error (approximately 2.39% ~ 33.5% reduction), lower relative errors (approximately 1.3% ~ 35.7% reduction) and higher correlations (approximately 2% ~ 690% increase) than traditional interpolation methods, including neighbor interpolation (NI) and spherical spline interpolation (SSI). In addition, the RESIT was integrated into the EEG preprocessing pipeline on the WeBrain cloud platform ( https://webrain.uestc.edu.cn/ ). These results suggest that the RESIT is a promising interpolation method for both separate and simultaneous EEG preprocessing that benefits further EEG analysis, including event-related potential (ERP) analysis, EEG network analysis, and strict group-level statistics.


Assuntos
Encéfalo , Couro Cabeludo , Eletrodos , Eletroencefalografia , Humanos , Padrões de Referência
11.
Neural Plast ; 2021: 6615384, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34054943

RESUMO

Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental brain disorders in childhood. Despite extensive researches, the neurobiological mechanism underlying ADHD is still left unveiled. Since the deficit functions, such as attention, have been demonstrated in ADHD, in our present study, based on the oddball P3 task, the corresponding electroencephalogram (EEG) of both healthy controls (HCs) and ADHD children was first collected. And we then not only focused on the event-related potential (ERP) evoked during tasks but also investigated related brain networks. Although an insignificant difference in behavior was found between the HCs and ADHD children, significant electrophysiological differences were found in both ERPs and brain networks. In detail, the dysfunctional attention occurred during the early stage of the designed task; as compared to HCs, the reduced P2 and N2 amplitudes in ADHD children were found, and the atypical information interaction might further underpin such a deficit. On the one hand, when investigating the cortical activity, HCs recruited much stronger brain activity mainly in the temporal and frontal regions, compared to ADHD children; on the other hand, the brain network showed atypical enhanced long-range connectivity between the frontal and occipital lobes but attenuated connectivity among frontal, parietal, and temporal lobes in ADHD children. We hope that the findings in this study may be instructive for the understanding of cognitive processing in children with ADHD.

12.
Adv Sci (Weinh) ; 8(10): 2004208, 2021 05.
Artigo em Inglês | MEDLINE | ID: mdl-34026450

RESUMO

The multi-mode pain-perceptual system (MMPPS) is essential for the human body to perceive noxious stimuli in all circumstances and make an appropriate reaction. Based on the central sensitization mechanism, the MMPPS can switch between different working modes and thus offers a smarter protection mechanism to human body. Accordingly, before injury MMPPS can offer warning of excessive pressure with normal pressure threshold. After injury, extra care on the periphery of damage will be activated by decreasing the pressure threshold. Furthermore, the MMPPS will gradually recover back to a normal state as damage heals. Although current devices can realize basic functions like damage localization and nociceptor signal imitating, the development of a human-like MMPPS is still a great challenge. Here, a bio-inspired MMPPS is developed for prosthetics protection, in which all working modes is realized and controlled by mimicking the central sensitization mechanism. Accordingly, the system warns one of a potential injury, identifies the damaged area, and subsequently offers extra care. The proposed system can open new avenues for designing next-generation prosthetics, especially make other smart sensing systems operate under complete protection against injuries.

13.
Brain Topogr ; 34(1): 78-87, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33128660

RESUMO

Tourette syndrome (TS) is a neuropsychiatric disorder with childhood onset characterized by chronic motor and vocal tics; however, the current diagnosis of TS patients is subjective, as it is mainly assessed based on the parents' description alongside specific evaluations. The early and accurate diagnosis of TS based on its potential symptoms in children would be of benefit in their future therapy, but reliable diagnoses are difficult due to the lack of objective knowledge of the etiology and pathogenesis of TS. In this study, resting-state electroencephalograms were first collected from 36 patients and 21 healthy controls (HCs); the corresponding resting-state functional networks were then constructed, and the potential differences in network topology between the two groups were extracted by using the topology of the spatial pattern of the network (SPN). Compared to the HCs, the TS patients exhibited decreased frontotemporal/occipital/parietal connectivity. When classifying the two groups, compared to the network properties, the derived SPN features achieved a much higher accuracy of 92.31%. The intrinsic long-range connectivity between the frontal and the temporal/occipital/parietal lobes was damaged in the patient group, and this dysfunctional network pattern might serve as a reliable biomarker to differentiate TS patients from HCs as well as to assess the severity of tic symptoms.


Assuntos
Tiques , Síndrome de Tourette , Criança , Eletroencefalografia , Humanos , Lobo Parietal/diagnóstico por imagem
14.
Artigo em Inglês | MEDLINE | ID: mdl-33216716

RESUMO

The acoustic stimulation influences of the brain is still unveiled, especially from the brain network point, which can reveal how interaction is propagated and integrated between different brain zones for chronic tinnitus patients. We specifically designed a paradigm to record the electroencephalograms (EEGs) for tinnitus patients when they were treated with consecutive acoustic stimulation neuromodulation therapy for up to 75 days, using the tinnitus handicap inventory (THI) to evaluate the tinnitus severity or the acoustic stimulation treatment efficacy, and the EEG to record the brain activities every 2 weeks. Then, we used an EEG-based coherence analysis to investigate if the changes in brain network consistent with the clinical outcomes can be observed during 75-days acoustic treatment. Finally, correlation analysis was conducted to study potential relationships between network properties and tinnitus handicap inventory score change. The EEG network became significantly weaker after long-term periodic acoustic stimulation treatment, and tinnitus handicap inventory score changes or the acoustic stimulation treatment efficacy are strongly correlated with the varying brain network properties. Long-term acoustic stimulation neuromodulation intervention can improve the rehabilitation of chronic tinnitus patients, and the EEG network provides a relatively reliable and quantitative analysis approach for objective evaluation of tinnitus clinical diagnosis and treatment.


Assuntos
Zumbido , Estimulação Acústica , Encéfalo , Eletroencefalografia , Humanos , Resultado do Tratamento
15.
Neural Plast ; 2020: 8851415, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33299398

RESUMO

Epileptic seizures are considered to be a brain network dysfunction, and chronic recurrent seizures can cause severe brain damage. However, the functional brain network underlying recurrent epileptic seizures is still left unveiled. This study is aimed at exploring the differences in a related brain activity before and after chronic repetitive seizures by investigating the power spectral density (PSD), fuzzy entropy, and functional connectivity in epileptic patients. The PSD analysis revealed differences between the two states at local area, showing postseizure energy accumulation. Besides, the fuzzy entropies of preseizure in the frontal, central, and temporal regions are higher than that of postseizure. Additionally, attenuated long-range connectivity and enhanced local connectivity were also found. Moreover, significant correlations were found between network metrics (i.e., characteristic path length and clustering coefficient) and individual seizure number. The PSD, fuzzy entropy, and network analysis may indicate that the brain is gradually impaired along with the occurrence of epilepsy, and the accumulated effect of brain impairment is observed in individuals with consecutive epileptic bursts. The findings of this study may provide helpful insights into understanding the network mechanism underlying chronic recurrent epilepsy.


Assuntos
Encéfalo/fisiopatologia , Eletroencefalografia , Epilepsia/fisiopatologia , Couro Cabeludo/fisiopatologia , Convulsões/fisiopatologia , Adolescente , Adulto , Criança , Eletroencefalografia/métodos , Epilepsia do Lobo Temporal/fisiopatologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Rede Nervosa/fisiopatologia , Adulto Jovem
16.
Entropy (Basel) ; 22(8)2020 Aug 14.
Artigo em Inglês | MEDLINE | ID: mdl-33286662

RESUMO

The accurate identification of an attention deficit hyperactivity disorder (ADHD) subject has remained a challenge for both neuroscience research and clinical diagnosis. Unfortunately, the traditional methods concerning the classification model and feature extraction usually depend on the single-channel model and static measurements (i.e., functional connectivity, FC) in the small, homogenous single-site dataset, which is limited and may cause the loss of intrinsic information in functional MRI (fMRI). In this study, we proposed a new two-stage network structure by combing a separated channel convolutional neural network (SC-CNN) with an attention-based network (SC-CNN-attention) to discriminate ADHD and healthy controls on a large-scale multi-site database (5 sites and n = 1019). To utilize both intrinsic temporal feature and the interactions of temporal dependent in whole-brain resting-state fMRI, in the first stage of our proposed network structure, a SC- CNN is used to learn the temporal feature of each brain region, and an attention network in the second stage is adopted to capture temporal dependent features among regions and extract fusion features. Using a "leave-one-site-out" cross-validation framework, our proposed method obtained a mean classification accuracy of 68.6% on five different sites, which is higher than those reported in previous studies. The classification results demonstrate that our proposed network is robust to data variants and is also replicated across sites. The combination of the SC-CNN with the attention network is powerful to capture the intrinsic fMRI information to discriminate ADHD across multi-site resting-state fMRI data.

17.
Neuropsychologia ; 148: 107655, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-33068599

RESUMO

A person's native language background exerts constraints on the brain's automatic responses while learning a second language. It remains unclear, however, whether and how musical experience may help the brain overcome such constraints and meet the requirements of a second language. This study compared native Chinese English learners who were musicians, non-musicians and native English readers on their automatic brain automatic integration of English letter-sounds with an ERP cross-modal audiovisual mismatch negativity paradigm. The results showed that native Chinese-speaking musicians successfully integrated English letters and sounds, but their non-musician peers did not, despite of their comparable English learning experience and proficiency level. However, native Chinese-speaking musicians demonstrated enhanced cross-modal MMN for both synchronized and delayed letter-sound integration, while native English readers only showed enhanced cross-modal MMN for synchronized integration. Moreover, native Chinese-speaking musicians showed stronger theta oscillations when integrating English letters and sounds, suggesting that they had better top-down modulation. In contrast, native English readers showed stronger delta oscillations for synchronized integration, and their cross-modal delta oscillations significantly correlated with English reading performance. These findings suggest that long-term professional musical experience may enhance the top-down modulation, then help the brain efficiently integrating letter-sounds required by the second language. Such benefits from musical experience may be different from those from specific language experience in shaping the brain's automatic responses to reading.


Assuntos
Música , Leitura , Estimulação Acústica , Encéfalo , Eletroencefalografia , Humanos , Idioma
18.
Aging Dis ; 11(2): 301-314, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-32257543

RESUMO

Recent advances in neuroimaging have demonstrated that patients with disorders of consciousness (DOC) may retain residual consciousness through activation of a complex functional brain network. However, an understanding of the hierarchy of residual consciousness and dynamic network connectivity in DOC patients is lacking. This study aimed to investigate residual consciousness and the dynamics of neural processing in DOC patients. We included 42 patients with DOC, categorized by aetiology. Event-related potentials combined with time-varying electroencephalography networks were used to probe affective consciousness in DOC and examine the related network mechanisms. The results showed an obvious frontal P3a component among patients in minimally conscious state (MCS), while a prominent N1 was observed in unresponsive wakefulness syndrome (UWS). No late positive potential (LPP) was detected in these patients. Next, we divided the results by aetiology. Patients with nontraumatic injury presented an obvious frontal P3a response compared to those with traumatic injury. With respect to the dynamic network mechanism, patients with UWS, both with and without trauma, exhibited impaired frontoparietal network connectivity during the middle to late emotion processing period (P3a and LPP). Surprisingly, unconscious post-traumatic patients had an evident deficit in top-down connectivity. This, it appears that early automatic sensory identification is preserved in UWS and that exogenous attention was preserved even in MCS. However, high-level cognitive abilities were severely attenuated in unconscious patients. We also speculate that reduced frontoparietal connectivity may be useful as a biomarker to distinguish patients in an MCS from those with UWS given the same aetiology.

19.
Neural Netw ; 125: 338-348, 2020 May.
Artigo em Inglês | MEDLINE | ID: mdl-32172143

RESUMO

A large-scale network provides a high hierarchical level for understanding the adaptive adjustment of the human brain during cognition processes. Since high spatial resolution is required, most of the related works are based on functional magnetic resonance imaging (fMRI); however, fMRI lacks the temporal information that is important in investigating the high cognition processes. Although combining electroencephalography (EEG) inverse solution and independent component analysis (ICA), researchers detected large-scale functional subnetworks recently, few researchers focus on the unreasonable negative activation, which is biased from the nonnegative electrical source activations in the brain. In this study, considering the favorable nonnegative property of Bayesian nonnegative matrix factorization (Bayesian NMF) and combining EEG source imaging, we developed a robust approach for EEG large-scale network construction and applied it to two independent real EEG datasets (i.e., decision-making and P300). Eight and nine best-fit networks, including such important subnetworks as the somatosensory-motor network (SMN), the default mode network (DMN), etc., were successfully identified for decision-making and P300, respectively. Compared to the networks acquired with ICA, these networks not only lacked confusing negative activations but also showed clear spatial distributions that are compatible with specific brain function. Based on the constructed large-scale network, we further probed that the self-referential network (SRN), the primary visual network (PVN), and the visual network (VN) demonstrated different interaction patterns with other networks between different responses in decision-making. Our results confirm the possibility of probing the neural mechanisms of high cognition processes at a very high temporal and spatial resolution level.


Assuntos
Córtex Cerebral/fisiologia , Eletroencefalografia/métodos , Modelos Neurológicos , Redes Neurais de Computação , Teorema de Bayes , Humanos , Vias Neurais/fisiologia
20.
Neural Netw ; 124: 213-222, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-32018159

RESUMO

The conventional multivariate Granger Analysis (GA) of directed interactions has been widely applied in brain network construction based on EEG recordings as well as fMRI. Nevertheless, EEG is usually inevitably contaminated by strong noise, which may cause network distortion due to the L2-norm used in GAs for directed network recovery. The Lp (p ≤1) norm has been shown to be more robust to outliers as compared to LASSO and L2-GAs. Motivated to construct the sparse brain networks under strong noise condition, we hereby introduce a new approach for GA analysis, termed LAPPS (Least Absolute LP (0

Assuntos
Eletroencefalografia/métodos , Córtex Motor/fisiologia , Redes Neurais de Computação , Feminino , Humanos , Masculino
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