Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 8 de 8
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
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
2.
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
3.
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.

4.
Opt Lett ; 44(2): 399-402, 2019 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-30644910

RESUMO

A emerging optical metasurface has raised wide interest due to its planar structure and unprecedented control of light through subwavelength nano structures. In this study, we propose a novel metalens that integrates the function of a concentrating lens and linear polarizer. For this lens, focus can only be formed under the incidence of designed linear polarization, and its focusing effect is significantly suppressed upon the incidence of the orthogonal polarization. The linear polarization distinguishing focus characteristic is from the special design of anisotropic subwavelength phase shifters with two functionalities. One is the space-variant polarization distinguishing phase profile achieved through the engineering of a three-dimensional anisotropic phase shifter structure. The other is the selective generation of scattering loss on the incidence of its orthorgonal polarization. The linear polarization distinguishing metalens is fabricated through a complementary metal-oxide-semiconductor compatible nano fabrication process, and its performance is demonstrated through both simulation and experiment.

5.
J Neurosci Methods ; 240: 170-8, 2015 Jan 30.
Artigo em Inglês | MEDLINE | ID: mdl-25448380

RESUMO

The autoregressive (AR) model is widely used in electroencephalogram (EEG) analyses such as waveform fitting, spectrum estimation, and system identification. In real applications, EEGs are inevitably contaminated with unexpected outlier artifacts, and this must be overcome. However, most of the current AR models are based on the L2 norm structure, which exaggerates the outlier effect due to the square property of the L2 norm. In this paper, a novel AR object function is constructed in the Lp (p≤1) norm space with the aim to compress the outlier effects on EEG analysis, and a fast iteration procedure is developed to solve this new AR model. The quantitative evaluation using simulated EEGs with outliers proves that the proposed Lp (p≤1) AR can estimate the AR parameters more robustly than the Yule-Walker, Burg and LS methods, under various simulated outlier conditions. The actual application to the resting EEG recording with ocular artifacts also demonstrates that Lp (p≤1) AR can effectively address the outliers and recover a resting EEG power spectrum that is more consistent with its physiological basis.


Assuntos
Eletroencefalografia/métodos , Algoritmos , Artefatos , Piscadela/fisiologia , Encéfalo/fisiologia , Simulação por Computador , Humanos , Masculino , Modelos Neurológicos , Descanso , Processamento de Sinais Assistido por Computador
6.
Neural Plast ; 2014: 180138, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25478236

RESUMO

Musicians undergoing long-term musical training show improved emotional and cognitive function, which suggests the presence of neuroplasticity. The structural and functional impacts of the human brain have been observed in musicians. In this study, we used data-driven functional connectivity analysis to map local and distant functional connectivity in resting-state functional magnetic resonance imaging data from 28 professional musicians and 28 nonmusicians. Compared with nonmusicians, musicians exhibited significantly greater local functional connectivity density in 10 regions, including the bilateral dorsal anterior cingulate cortex, anterior insula, and anterior temporoparietal junction. A distant functional connectivity analysis demonstrated that most of these regions were included in salience system, which is associated with high-level cognitive control and fundamental attentional process. Additionally, musicians had significantly greater functional integration in this system, especially for connections to the left insula. Increased functional connectivity between the left insula and right temporoparietal junction may be a response to long-term musical training. Our findings indicate that the improvement of salience network is involved in musical training. The salience system may represent a new avenue for exploration regarding the underlying foundations of enhanced higher-level cognitive processes in musicians.


Assuntos
Atenção/fisiologia , Encéfalo/fisiologia , Música , Rede Nervosa/fisiologia , Plasticidade Neuronal , Adulto , Mapeamento Encefálico , Cognição/fisiologia , Feminino , Humanos , Imageamento por Ressonância Magnética , Masculino , Adulto Jovem
7.
PLoS One ; 9(8): e105508, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25157896

RESUMO

PURPOSE: Musicians experience a large amount of information transfer and integration of complex sensory, motor, and auditory processes when training and playing musical instruments. Therefore, musicians are a useful model in which to investigate neural adaptations in the brain. METHODS: Here, based on diffusion-weighted imaging, probabilistic tractography was used to determine the architecture of white matter anatomical networks in musicians and non-musicians. Furthermore, the features of the white matter networks were analyzed using graph theory. RESULTS: Small-world properties of the white matter network were observed in both groups. Compared with non-musicians, the musicians exhibited significantly increased connectivity strength in the left and right supplementary motor areas, the left calcarine fissure and surrounding cortex and the right caudate nucleus, as well as a significantly larger weighted clustering coefficient in the right olfactory cortex, the left medial superior frontal gyrus, the right gyrus rectus, the left lingual gyrus, the left supramarginal gyrus, and the right pallidum. Furthermore, there were differences in the node betweenness centrality in several regions. However, no significant differences in topological properties were observed at a global level. CONCLUSIONS: We illustrated preliminary findings to extend the network level understanding of white matter plasticity in musicians who have had long-term musical training. These structural, network-based findings may indicate that musicians have enhanced information transmission efficiencies in local white matter networks that are related to musical training.


Assuntos
Encéfalo/anatomia & histologia , Música , Rede Nervosa , Adulto , Imagem de Tensor de Difusão , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Masculino , Probabilidade , Substância Branca/anatomia & histologia , Adulto Jovem
8.
Physiol Meas ; 35(7): 1279-98, 2014 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-24853724

RESUMO

The diagnosis of mild cognitive impairment (MCI) is very helpful for early therapeutic interventions of Alzheimer's disease (AD). MCI has been proven to be correlated with disorders in multiple brain areas. In this paper, we used information from resting brain networks at different EEG frequency bands to reliably recognize MCI. Because EEG network analysis is influenced by the reference that is used, we also evaluate the effect of the reference choices on the resting scalp EEG network-based MCI differentiation. The conducted study reveals two aspects: (1) the network-based MCI differentiation is superior to the previously reported classification that uses coherence in the EEG; and (2) the used EEG reference influences the differentiation performance, and the zero approximation technique (reference electrode standardization technique, REST) can construct a more accurate scalp EEG network, which results in a higher differentiation accuracy for MCI. This study indicates that the resting scalp EEG-based network analysis could be valuable for MCI recognition in the future.


Assuntos
Encéfalo/fisiopatologia , Disfunção Cognitiva/diagnóstico , Disfunção Cognitiva/fisiopatologia , Eletroencefalografia/métodos , Processamento de Sinais Assistido por Computador , Idoso , Feminino , Humanos , Masculino , Curva ROC , Descanso , Couro Cabeludo
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...