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1.
PLoS One ; 19(7): e0299784, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38950011

RESUMO

Observers can discriminate between correct versus incorrect perceptual decisions with feelings of confidence. The centro-parietal positivity build-up rate (CPP slope) has been suggested as a likely neural signature of accumulated evidence, which may guide both perceptual performance and confidence. However, CPP slope also covaries with reaction time, which also covaries with confidence in previous studies, and performance and confidence typically covary; thus, CPP slope may index signatures of perceptual performance rather than confidence per se. Moreover, perceptual metacognition-including neural correlates-has largely been studied in vision, with few exceptions. Thus, we lack understanding of domain-general neural signatures of perceptual metacognition outside vision. Here we designed a novel auditory pitch identification task and collected behavior with simultaneous 32-channel EEG in healthy adults. Participants saw two tone labels which varied in tonal distance on each trial (e.g., C vs D, C vs F), then heard a single auditory tone; they identified which label was correct and rated confidence. We found that pitch identification confidence varied with tonal distance, but performance, metacognitive sensitivity (trial-by-trial covariation of confidence with accuracy), and reaction time did not. Interestingly, however, while CPP slope covaried with performance and reaction time, it did not significantly covary with confidence. We interpret these results to mean that CPP slope is likely a signature of first-order perceptual processing and not confidence-specific signals or computations in auditory tasks. Our novel pitch identification task offers a valuable method to examine the neural correlates of auditory and domain-general perceptual confidence.


Assuntos
Eletroencefalografia , Percepção da Altura Sonora , Tempo de Reação , Humanos , Masculino , Feminino , Adulto , Tempo de Reação/fisiologia , Adulto Jovem , Percepção da Altura Sonora/fisiologia , Estimulação Acústica , Metacognição/fisiologia , Percepção Auditiva/fisiologia
2.
PeerJ ; 12: e17623, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38952974

RESUMO

Although exercise training has been shown to enhance neurological function, there is a shortage of research on how exercise training affects the temporal-spatial synchronization properties of functional networks, which are crucial to the neurological system. This study recruited 23 professional and 24 amateur dragon boat racers to perform simulated paddling on ergometers while recording EEG. The spatiotemporal dynamics of the brain were analyzed using microstates and omega complexity. Temporal dynamics results showed that microstate D, which is associated with attentional networks, appeared significantly altered, with significantly higher duration, occurrence, and coverage in the professional group than in the amateur group. The transition probabilities of microstate D exhibited a similar pattern. The spatial dynamics results showed the professional group had lower brain complexity than the amateur group, with a significant decrease in omega complexity in the α (8-12 Hz) and ß (13-30 Hz) bands. Dragon boat training may strengthen the attentive network and reduce the complexity of the brain. This study provides evidence that dragon boat exercise improves the efficiency of the cerebral functional networks on a spatiotemporal scale.


Assuntos
Encéfalo , Eletroencefalografia , Humanos , Masculino , Eletroencefalografia/métodos , Encéfalo/fisiologia , Adulto , Adulto Jovem , Exercício Físico/fisiologia , Esportes Aquáticos/fisiologia , Atenção/fisiologia , Feminino
3.
PeerJ ; 12: e17622, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38952977

RESUMO

Introduction: High velocity thrust manipulation is commonly used when managing joint dysfunctions. Often, these thrust maneuvers will elicit an audible pop. It has been unclear what conclusively causes this audible sound and its clinical meaningfulness. This study sought to identify the effect of the audible pop on brainwave activity directly following a prone T7 thrust manipulation in asymptomatic/healthy subjects. Methods: This was a quasi-experimental repeated measure study design in which 57 subjects completed the study protocol. Brain wave activity was measured with the Emotiv EPOC+, which collects data with a frequency of 128 HZ and has 14 electrodes. Testing was performed in a controlled environment with minimal electrical interference (as measured with a Gauss meter), temperature variance, lighting variance, sound pollution, and other variable changes that could have influenced or interfered with pure EEG data acquisition. After accommodation each subject underwent a prone T7 posterior-anterior thrust manipulation. Immediately after the thrust manipulation the brainwave activity was measured for 10 seconds. Results: The non-audible group (N = 20) consisted of 55% males, and the audible group (N = 37) consisted of 43% males. The non-audible group EEG data revealed a significant change in brain wave activity under some of the electrodes in the frontal, parietal, and the occipital lobes. In the audible group, there was a significant change in brain wave activity under all electrodes in the frontal lobes, the parietal lobe, and the occipital lobes but not the temporal lobes. Conclusion: The audible sounds caused by a thoracic high velocity thrust manipulation did not affect the activity in the audible centers in the temporal brain region. The results support the hypothesis that thrust manipulation with or without audible sound results in a generalized relaxation immediately following the manipulation. The absence of a significant difference in brainwave activity in the frontal lobe in this study might indicate that the audible pop does not produce a "placebo" mechanism.


Assuntos
Manipulação da Coluna , Humanos , Masculino , Feminino , Adulto , Manipulação da Coluna/métodos , Ondas Encefálicas/fisiologia , Eletroencefalografia/métodos , Adulto Jovem , Som
4.
IEEE J Biomed Health Inform ; 28(7): 3872-3881, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38954558

RESUMO

Electroencephalogram (EEG) has been widely utilized in emotion recognition due to its high temporal resolution and reliability. However, the individual differences and non-stationary characteristics of EEG, along with the complexity and variability of emotions, pose challenges in generalizing emotion recognition models across subjects. In this paper, an end-to-end framework is proposed to improve the performance of cross-subject emotion recognition. A novel evolutionary programming (EP)-based optimization strategy with neural network (NN) as the base classifier termed NN ensemble with EP (EPNNE) is designed for cross-subject emotion recognition. The effectiveness of the proposed method is evaluated on the publicly available DEAP, FACED, SEED, and SEED-IV datasets. Numerical results demonstrate that the proposed method is superior to state-of-the-art cross-subject emotion recognition methods. The proposed end-to-end framework for cross-subject emotion recognition aids biomedical researchers in effectively assessing individual emotional states, thereby enabling efficient treatment and interventions.


Assuntos
Eletroencefalografia , Emoções , Processamento de Sinais Assistido por Computador , Humanos , Eletroencefalografia/métodos , Emoções/fisiologia , Redes Neurais de Computação , Aprendizado de Máquina , Algoritmos , Reconhecimento Automatizado de Padrão/métodos , Bases de Dados Factuais , Adulto , Feminino , Masculino
5.
Commun Biol ; 7(1): 790, 2024 Jun 29.
Artigo em Inglês | MEDLINE | ID: mdl-38951602

RESUMO

Neuroscience research has shown that specific brain patterns can relate to creativity during multiple tasks but also at rest. Nevertheless, the electrophysiological correlates of a highly creative brain remain largely unexplored. This study aims to uncover resting-state networks related to creative behavior using high-density electroencephalography (HD-EEG) and to test whether the strength of functional connectivity within these networks could predict individual creativity in novel subjects. We acquired resting state HD-EEG data from 90 healthy participants who completed a creative behavior inventory. We then employed connectome-based predictive modeling; a machine-learning technique that predicts behavioral measures from brain connectivity features. Using a support vector regression, our results reveal functional connectivity patterns related to high and low creativity, in the gamma frequency band (30-45 Hz). In leave-one-out cross-validation, the combined model of high and low networks predicts individual creativity with very good accuracy (r = 0.36, p = 0.00045). Furthermore, the model's predictive power is established through external validation on an independent dataset (N = 41), showing a statistically significant correlation between observed and predicted creativity scores (r = 0.35, p = 0.02). These findings reveal large-scale networks that could predict creative behavior at rest, providing a crucial foundation for developing HD-EEG-network-based markers of creativity.


Assuntos
Encéfalo , Criatividade , Eletroencefalografia , Descanso , Humanos , Eletroencefalografia/métodos , Masculino , Feminino , Adulto , Encéfalo/fisiologia , Adulto Jovem , Descanso/fisiologia , Conectoma/métodos
6.
Brain Behav ; 14(7): e3597, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38956811

RESUMO

INTRODUCTION: Chemosensory function in pregnant women is far from being fully understood due to the lack of data and inconsistencies between the results of self-reports and objective studies. METHODS: In the present study in pregnant and non-pregnant women (npregnant = 14, nnon-pregnant = 13), we measured EEG-derived electrophysiological response measures supported by psychophysical olfactory and trigeminal tests. RESULTS: Results indicate that the olfactory event-related potential amplitudes or latencies of the P1, N1, and P2 components remain unchanged in pregnant women. In accordance with these findings, no difference was observed between pregnant and non-pregnant women in psychophysical olfactory tests. However, pregnant women displayed a lower degree of sensitivity to trigeminal stimuli compared to non-pregnant controls, which was also reflected in the electrophysiological responses to trigeminal stimuli. CONCLUSION: Counterintuitive as they may seem, our findings demonstrate a "flattening" of chemosomatosensory responses. Psychological processes occurring during pregnancy, such as changes in socioemotional perception of odors resulting from the diminished stress response, may provide a background to these results. Overall, the present results indicate the absence of major differences between non-pregnant and pregnant women in terms of measured olfactory function though chemosomatosensory function of the pregnant women appears to be decreased.


Assuntos
Eletroencefalografia , Nervo Trigêmeo , Humanos , Feminino , Gravidez , Adulto , Nervo Trigêmeo/fisiologia , Eletroencefalografia/métodos , Potenciais Evocados/fisiologia , Adulto Jovem , Percepção Olfatória/fisiologia , Olfato/fisiologia , Odorantes
7.
PLoS One ; 19(7): e0290142, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38959207

RESUMO

AIM: This preliminary study investigated the differences in event-related potential and reaction time under two groups (athletes vs. non-athletes). MATERIAL AND METHODS: The P300 was analyzed for Fz, Cz, and Pz electrodes in thirty-one healthy volunteers divided into two groups (volleyball athletes and non-athletes). In addition, the participants performed a saccadic eye movement task to measure reaction time. RESULTS: The EEG analysis showed that the athletes, in comparison to the no-athletes, have differences in the P300 in the frontal area (p = 0.021). In relation to reaction time, the results show lower reaction time for athletes (p = 0.001). CONCLUSIONS: The volleyball athletes may present a greater allocation of attention during the execution of the inhibition task, since they have a lower reaction time for responses when compared to non-athletes.


Assuntos
Atletas , Eletroencefalografia , Tempo de Reação , Movimentos Sacádicos , Voleibol , Humanos , Tempo de Reação/fisiologia , Movimentos Sacádicos/fisiologia , Voleibol/fisiologia , Masculino , Feminino , Adulto Jovem , Adulto , Potenciais Evocados/fisiologia , Potenciais Evocados P300/fisiologia , Atenção/fisiologia
8.
PLoS One ; 19(7): e0305864, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38959272

RESUMO

This research aims to establish a practical stress detection framework by integrating physiological indicators and deep learning techniques. Utilizing a virtual reality (VR) interview paradigm mirroring real-world scenarios, our focus is on classifying stress states through accessible single-channel electroencephalogram (EEG) and galvanic skin response (GSR) data. Thirty participants underwent stress-inducing VR interviews, with biosignals recorded for deep learning models. Five convolutional neural network (CNN) architectures and one Vision Transformer model, including a multiple-column structure combining EEG and GSR features, showed heightened predictive capabilities and an enhanced area under the receiver operating characteristic curve (AUROC) in stress prediction compared to single-column models. Our experimental protocol effectively elicited stress responses, observed through fluctuations in stress visual analogue scale (VAS), EEG, and GSR metrics. In the single-column architecture, ResNet-152 excelled with a GSR AUROC of 0.944 (±0.027), while the Vision Transformer performed well in EEG, achieving peak AUROC values of 0.886 (±0.069) respectively. Notably, the multiple-column structure, based on ResNet-50, achieved the highest AUROC value of 0.954 (±0.018) in stress classification. Through VR-based simulated interviews, our study induced social stress responses, leading to significant modifications in GSR and EEG measurements. Deep learning models precisely classified stress levels, with the multiple-column strategy demonstrating superiority. Additionally, discreetly placing single-channel EEG measurements behind the ear enhances the convenience and accuracy of stress detection in everyday situations.


Assuntos
Aprendizado Profundo , Eletroencefalografia , Resposta Galvânica da Pele , Estresse Psicológico , Realidade Virtual , Humanos , Eletroencefalografia/métodos , Feminino , Masculino , Adulto , Estresse Psicológico/fisiopatologia , Estresse Psicológico/diagnóstico , Resposta Galvânica da Pele/fisiologia , Adulto Jovem , Curva ROC , Redes Neurais de Computação
9.
Artigo em Inglês | MEDLINE | ID: mdl-38949929

RESUMO

Approximately one third of the population is prone to motion sickness (MS), which is associated with the dysfunction in the integration of sensory inputs. Transcranial alternating current stimulation (tACS) has been widely used to modulate neurological functions by affecting neural oscillation. However, it has not been applied in the treatment of motion sickness. This study aims to investigate changes in brain oscillations during exposure to MS stimuli and to further explore the potential impact of tACS with the corresponding frequency and site on MS symptoms. A total of 19 subjects were recruited to be exposed to Coriolis stimuli to complete an inducing session. After that, they were randomly assigned to tACS stimulation group or sham stimulation group to complete a stimulation session. Electroencephalography (EEG), electrocardiogram, and galvanic skin response were recorded during the experiment. All the subjects suffering from obvious MS symptoms after inducing session were observed that alpha power of four channels of parieto-occipital lobe significantly decreased (P7: t =3.589, p <0.001; P8: t =2.667, p <0.05; O1: t =3.556, p <0.001; O2: t =2.667, p <0.05). Based on this, tACS group received the tACS stimulation at 10Hz from Oz to CPz. Compared to sham group, tACS stimulation significantly improved behavioral performance and entrained the alpha oscillation in individuals whose alpha power decrease during the inducing session. The findings show that parieto-occipital alpha oscillation plays a critical role in the integration of sensory inputs, and alpha tACS on parieto-occipital can become a potential method to mitigate MS symptoms.


Assuntos
Ritmo alfa , Eletroencefalografia , Resposta Galvânica da Pele , Enjoo devido ao Movimento , Lobo Occipital , Lobo Parietal , Estimulação Transcraniana por Corrente Contínua , Humanos , Enjoo devido ao Movimento/prevenção & controle , Enjoo devido ao Movimento/fisiopatologia , Masculino , Lobo Occipital/fisiologia , Feminino , Lobo Parietal/fisiologia , Adulto , Estimulação Transcraniana por Corrente Contínua/métodos , Adulto Jovem , Resposta Galvânica da Pele/fisiologia , Eletrocardiografia
10.
Artigo em Inglês | MEDLINE | ID: mdl-38949928

RESUMO

Brain-computer interfaces (BCIs) provide a communication interface between the brain and external devices and have the potential to restore communication and control in patients with neurological injury or disease. For the invasive BCIs, most studies recruited participants from hospitals requiring invasive device implantation. Three widely used clinical invasive devices that have the potential for BCIs applications include surface electrodes used in electrocorticography (ECoG) and depth electrodes used in Stereo-electroencephalography (SEEG) and deep brain stimulation (DBS). This review focused on BCIs research using surface (ECoG) and depth electrodes (including SEEG, and DBS electrodes) for movement decoding on human subjects. Unlike previous reviews, the findings presented here are from the perspective of the decoding target or task. In detail, five tasks will be considered, consisting of the kinematic decoding, kinetic decoding,identification of body parts, dexterous hand decoding, and motion intention decoding. The typical studies are surveyed and analyzed. The reviewed literature demonstrated a distributed motor-related network that spanned multiple brain regions. Comparison between surface and depth studies demonstrated that richer information can be obtained using surface electrodes. With regard to the decoding algorithms, deep learning exhibited superior performance using raw signals than traditional machine learning algorithms. Despite the promising achievement made by the open-loop BCIs, closed-loop BCIs with sensory feedback are still in their early stage, and the chronic implantation of both ECoG surface and depth electrodes has not been thoroughly evaluated.


Assuntos
Interfaces Cérebro-Computador , Eletrocorticografia , Eletrodos Implantados , Movimento , Humanos , Eletrocorticografia/instrumentação , Eletrocorticografia/métodos , Movimento/fisiologia , Estimulação Encefálica Profunda/instrumentação , Fenômenos Biomecânicos , Eletroencefalografia/métodos , Eletroencefalografia/instrumentação , Eletrodos , Córtex Motor/fisiologia , Mãos/fisiologia , Algoritmos
11.
PLoS One ; 19(7): e0304413, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38954679

RESUMO

BACKGROUND: Sedatives are commonly used to promote sleep in intensive care unit patients. However, it is not clear whether sedation-induced states are similar to the biological sleep. We explored if sedative-induced states resemble biological sleep using multichannel electroencephalogram (EEG) recordings. METHODS: Multichannel EEG datasets from two different sources were used in this study: (1) sedation dataset consisting of 102 healthy volunteers receiving propofol (N = 36), sevoflurane (N = 36), or dexmedetomidine (N = 30), and (2) publicly available sleep EEG dataset (N = 994). Forty-four quantitative time, frequency and entropy features were extracted from EEG recordings and were used to train the machine learning algorithms on sleep dataset to predict sleep stages in the sedation dataset. The predicted sleep states were then compared with the Modified Observer's Assessment of Alertness/ Sedation (MOAA/S) scores. RESULTS: The performance of the model was poor (AUC = 0.55-0.58) in differentiating sleep stages during propofol and sevoflurane sedation. In the case of dexmedetomidine, the AUC of the model increased in a sedation-dependent manner with NREM stages 2 and 3 highly correlating with deep sedation state reaching an AUC of 0.80. CONCLUSIONS: We addressed an important clinical question to identify biological sleep promoting sedatives using EEG signals. We demonstrate that propofol and sevoflurane do not promote EEG patterns resembling natural sleep while dexmedetomidine promotes states resembling NREM stages 2 and 3 sleep, based on current sleep staging standards.


Assuntos
Dexmedetomidina , Eletroencefalografia , Hipnóticos e Sedativos , Aprendizado de Máquina , Propofol , Sevoflurano , Sono , Humanos , Hipnóticos e Sedativos/farmacologia , Hipnóticos e Sedativos/administração & dosagem , Masculino , Adulto , Feminino , Sono/efeitos dos fármacos , Sono/fisiologia , Propofol/farmacologia , Propofol/administração & dosagem , Sevoflurano/farmacologia , Sevoflurano/efeitos adversos , Sevoflurano/administração & dosagem , Dexmedetomidina/farmacologia , Fases do Sono/efeitos dos fármacos , Adulto Jovem
12.
Sci Data ; 11(1): 718, 2024 Jul 02.
Artigo em Inglês | MEDLINE | ID: mdl-38956046

RESUMO

Handwritten signatures in biometric authentication leverage unique individual characteristics for identification, offering high specificity through dynamic and static properties. However, this modality faces significant challenges from sophisticated forgery attempts, underscoring the need for enhanced security measures in common applications. To address forgery in signature-based biometric systems, integrating a forgery-resistant modality, namely, noninvasive electroencephalography (EEG), which captures unique brain activity patterns, can significantly enhance system robustness by leveraging multimodality's strengths. By combining EEG, a physiological modality, with handwritten signatures, a behavioral modality, our approach capitalizes on the strengths of both, significantly fortifying the robustness of biometric systems through this multimodal integration. In addition, EEG's resistance to replication offers a high-security level, making it a robust addition to user identification and verification. This study presents a new multimodal SignEEG v1.0 dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected with Emotiv Insight and Wacom One sensors, respectively. The multimodal data consists of three paradigms based on mental, & motor imagery, and physical execution: i) thinking of the signature's image, (ii) drawing the signature mentally, and (iii) drawing a signature physically. Extensive experiments have been conducted to establish a baseline with machine learning classifiers. The results demonstrate that multimodality in biometric systems significantly enhances robustness, achieving high reliability even with limited sample sizes. We release the raw, pre-processed data and easy-to-follow implementation details.


Assuntos
Eletroencefalografia , Humanos , Escrita Manual , Identificação Biométrica/métodos , Biometria
13.
Sci Rep ; 14(1): 15111, 2024 07 02.
Artigo em Inglês | MEDLINE | ID: mdl-38956186

RESUMO

Recent studies have shown a growing interest in the so-called "aperiodic" component of the EEG power spectrum, which describes the overall trend of the whole spectrum with a linear or exponential function. In the field of brain aging, this aperiodic component is associated both with age-related changes and performance on cognitive tasks. This study aims to elucidate the potential role of education in moderating the relationship between resting-state EEG features (including aperiodic component) and cognitive performance in aging. N = 179 healthy participants of the "Leipzig Study for Mind-Body-Emotion Interactions" (LEMON) dataset were divided into three groups based on age and education. Older adults exhibited lower exponent, offset (i.e. measures of aperiodic component), and Individual Alpha Peak Frequency (IAPF) as compared to younger adults. Moreover, visual attention and working memory were differently associated with the aperiodic component depending on education: in older adults with high education, higher exponent predicted slower processing speed and less working memory capacity, while an opposite trend was found in those with low education. While further investigation is needed, this study shows the potential modulatory role of education in the relationship between the aperiodic component of the EEG power spectrum and aging cognition.


Assuntos
Envelhecimento , Cognição , Eletroencefalografia , Humanos , Cognição/fisiologia , Masculino , Feminino , Idoso , Envelhecimento/fisiologia , Adulto , Pessoa de Meia-Idade , Memória de Curto Prazo/fisiologia , Adulto Jovem , Encéfalo/fisiologia , Escolaridade , Atenção/fisiologia , Idoso de 80 Anos ou mais
14.
Sci Rep ; 14(1): 15154, 2024 07 02.
Artigo em Inglês | MEDLINE | ID: mdl-38956297

RESUMO

Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incorporating multiple frequency bands should be more efficient in associating potential EEG biomarkers with first-episode psychosis (FEP), leading to an accurate diagnosis. We explore multiple machine-learning methods, including random-forest, support vector machine, and Gaussian process classifier (GPC), to demonstrate the practicality of resting-state power spectral density (PSD) to distinguish patients of FEP from healthy controls. A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper. The GPC model outperforms the other models with a specificity of 95.78% to show that PSD can be used as an effective feature extraction technique for analyzing and classifying resting-state EEG signals of psychiatric disorders.


Assuntos
Eletroencefalografia , Transtornos Psicóticos , Máquina de Vetores de Suporte , Humanos , Transtornos Psicóticos/fisiopatologia , Transtornos Psicóticos/diagnóstico , Eletroencefalografia/métodos , Feminino , Masculino , Adulto , Adulto Jovem , Descanso/fisiologia , Aprendizado de Máquina , Encéfalo/fisiopatologia , Adolescente , Processamento de Sinais Assistido por Computador
15.
BMC Bioinformatics ; 25(1): 227, 2024 Jul 02.
Artigo em Inglês | MEDLINE | ID: mdl-38956454

RESUMO

BACKGROUND: Multivariate synchronization index (MSI) has been successfully applied for frequency detection in steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems. However, the standard MSI algorithm and its variants cannot simultaneously take full advantage of the time-local structure and the harmonic components in SSVEP signals, which are both crucial for frequency detection performance. To overcome the limitation, we propose a novel filter bank temporally local MSI (FBTMSI) algorithm to further improve SSVEP frequency detection accuracy. The method explicitly utilizes the temporal information of signal for covariance matrix estimation and employs filter bank decomposition to exploits SSVEP-related harmonic components. RESULTS: We employed the cross-validation strategy on the public Benchmark dataset to optimize the parameters and evaluate the performance of the FBTMSI algorithm. Experimental results show that FBTMSI outperforms the standard MSI, temporally local MSI (TMSI) and filter bank driven MSI (FBMSI) algorithms across multiple experimental settings. In the case of data length of one second, the average accuracy of FBTMSI is 9.85% and 3.15% higher than that of the FBMSI and the TMSI, respectively. CONCLUSIONS: The promising results demonstrate the effectiveness of the FBTMSI algorithm for frequency recognition and show its potential in SSVEP-based BCI applications.


Assuntos
Algoritmos , Interfaces Cérebro-Computador , Eletroencefalografia , Potenciais Evocados Visuais , Humanos , Potenciais Evocados Visuais/fisiologia , Eletroencefalografia/métodos , Processamento de Sinais Assistido por Computador
16.
Cereb Cortex ; 34(7)2024 Jul 03.
Artigo em Inglês | MEDLINE | ID: mdl-38967041

RESUMO

Autonomic symptoms in Parkinson's disease result from variable involvement of the central and peripheral systems, but many aspects remain unclear. The analysis of functional connectivity has shown promising results in assessing the pathophysiology of Parkinson's disease. This study aims to investigate the association between autonomic symptoms and cortical functional connectivity in early Parkinson's disease patients using high-density EEG. 53 early Parkinson's disease patients (F/M 18/35) and 49 controls (F/M 20/29) were included. Autonomic symptoms were evaluated using the Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score. Data were recorded with a 64-channel EEG system. We analyzed cortical functional connectivity, based on weighted phase-lag index, in θ-α-ß-low-γ bands. A network-based statistic was used to perform linear regression between Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score and functional connectivity in Parkinson's disease patients. We observed a positive relation between the Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction score and α-functional connectivity (network τ = 2.8, P = 0.038). Regions with higher degrees were insula and limbic lobe. Moreover, we found positive correlations between the mean connectivity of this network and the gastrointestinal, cardiovascular, and thermoregulatory domains of Scales for Outcomes in Parkinson's disease-Autonomic Dysfunction. Our results revealed abnormal functional connectivity in specific areas in Parkinson's disease patients with greater autonomic symptoms. Insula and limbic areas play a significant role in the regulation of the autonomic system. Increased functional connectivity in these regions might represent the central compensatory mechanism of peripheral autonomic dysfunction in Parkinson's disease.


Assuntos
Doenças do Sistema Nervoso Autônomo , Eletroencefalografia , Doença de Parkinson , Humanos , Doença de Parkinson/fisiopatologia , Doença de Parkinson/diagnóstico por imagem , Doença de Parkinson/complicações , Feminino , Masculino , Pessoa de Meia-Idade , Idoso , Doenças do Sistema Nervoso Autônomo/fisiopatologia , Doenças do Sistema Nervoso Autônomo/etiologia , Córtex Insular/diagnóstico por imagem , Córtex Insular/fisiopatologia , Sistema Límbico/fisiopatologia , Sistema Límbico/diagnóstico por imagem , Vias Neurais/fisiopatologia , Vias Neurais/diagnóstico por imagem
17.
BMC Neurosci ; 25(1): 30, 2024 Jul 04.
Artigo em Inglês | MEDLINE | ID: mdl-38965489

RESUMO

BACKGROUND: Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the two most common neurodegenerative dementias, presenting with similar clinical features that challenge accurate diagnosis. Despite extensive research, the underlying pathophysiological mechanisms remain unclear, and effective treatments are limited. This study aims to investigate the alterations in brain network connectivity associated with AD and FTD to enhance our understanding of their pathophysiology and establish a scientific foundation for their diagnosis and treatment. METHODS: We analyzed preprocessed electroencephalogram (EEG) data from the OpenNeuro public dataset, comprising 36 patients with AD, 23 patients with FTD, and 29 healthy controls (HC). Participants were in a resting state with eyes closed. We estimated the average functional connectivity using the Phase Lag Index (PLI) for lower frequencies (delta and theta) and the Amplitude Envelope Correlation with leakage correction (AEC-c) for higher frequencies (alpha, beta, and gamma). Graph theory was applied to calculate topological parameters, including mean node degree, clustering coefficient, characteristic path length, global and local efficiency. A permutation test was then utilized to assess changes in brain network connectivity in AD and FTD based on these parameters. RESULTS: Both AD and FTD patients showed increased mean PLI values in the theta frequency band, along with increases in average node degree, clustering coefficient, global efficiency, and local efficiency. Conversely, mean AEC-c values in the alpha frequency band were notably diminished, which was accompanied by decreases average node degree, clustering coefficient, global efficiency, and local efficiency. Furthermore, AD patients in the occipital region showed an increase in theta band node degree and decreased alpha band clustering coefficient and local efficiency, a pattern not observed in FTD. CONCLUSIONS: Our findings reveal distinct abnormalities in the functional network topology and connectivity in AD and FTD, which may contribute to a better understanding of the pathophysiological mechanisms of these diseases. Specifically, patients with AD demonstrated a more widespread change in functional connectivity, while those with FTD retained connectivity in the occipital lobe. These observations could provide valuable insights for developing electrophysiological markers to differentiate between the two diseases.


Assuntos
Doença de Alzheimer , Encéfalo , Eletroencefalografia , Demência Frontotemporal , Humanos , Demência Frontotemporal/fisiopatologia , Doença de Alzheimer/fisiopatologia , Feminino , Masculino , Idoso , Eletroencefalografia/métodos , Encéfalo/fisiopatologia , Pessoa de Meia-Idade , Rede Nervosa/fisiopatologia , Rede Nervosa/diagnóstico por imagem , Vias Neurais/fisiopatologia
18.
PLoS One ; 19(7): e0298110, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38968195

RESUMO

Neuroimaging studies have suggested an important role for the default mode network (DMN) in disorders of consciousness (DoC). However, the extent to which DMN connectivity can discriminate DoC states-unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS)-is less evident. Particularly, it is unclear whether effective DMN connectivity, as measured indirectly with dynamic causal modelling (DCM) of resting EEG can disentangle UWS from healthy controls and from patients considered conscious (MCS+). Crucially, this extends to UWS patients with potentially "covert" awareness (minimally conscious star, MCS*) indexed by voluntary brain activity in conjunction with partially preserved frontoparietal metabolism as measured with positron emission tomography (PET+ diagnosis; in contrast to PET- diagnosis with complete frontoparietal hypometabolism). Here, we address this gap by using DCM of EEG data acquired from patients with traumatic brain injury in 11 UWS (6 PET- and 5 PET+) and in 12 MCS+ (11 PET+ and 1 PET-), alongside with 11 healthy controls. We provide evidence for a key difference in left frontoparietal connectivity when contrasting UWS PET- with MCS+ patients and healthy controls. Next, in a leave-one-subject-out cross-validation, we tested the classification performance of the DCM models demonstrating that connectivity between medial prefrontal and left parietal sources reliably discriminates UWS PET- from MCS+ patients and controls. Finally, we illustrate that these models generalize to an unseen dataset: models trained to discriminate UWS PET- from MCS+ and controls, classify MCS* patients as conscious subjects with high posterior probability (pp > .92). These results identify specific alterations in the DMN after severe brain injury and highlight the clinical utility of EEG-based effective connectivity for identifying patients with potential covert awareness.


Assuntos
Transtornos da Consciência , Estado de Consciência , Eletroencefalografia , Lobo Parietal , Humanos , Masculino , Feminino , Adulto , Eletroencefalografia/métodos , Pessoa de Meia-Idade , Lobo Parietal/fisiopatologia , Lobo Parietal/diagnóstico por imagem , Transtornos da Consciência/fisiopatologia , Transtornos da Consciência/diagnóstico por imagem , Estado de Consciência/fisiologia , Tomografia por Emissão de Pósitrons , Lobo Frontal/diagnóstico por imagem , Lobo Frontal/fisiopatologia , Lesões Encefálicas Traumáticas/fisiopatologia , Lesões Encefálicas Traumáticas/diagnóstico por imagem , Estado Vegetativo Persistente/fisiopatologia , Estado Vegetativo Persistente/diagnóstico por imagem , Estudos de Coortes , Estudos de Casos e Controles , Adulto Jovem , Rede Nervosa/fisiopatologia , Rede Nervosa/diagnóstico por imagem
19.
Elife ; 122024 Jul 08.
Artigo em Inglês | MEDLINE | ID: mdl-38976325

RESUMO

In patients suffering absence epilepsy, recurring seizures can significantly decrease their quality of life and lead to yet untreatable comorbidities. Absence seizures are characterized by spike-and-wave discharges on the electroencephalogram associated with a transient alteration of consciousness. However, it is still unknown how the brain responds to external stimuli during and outside of seizures. This study aimed to investigate responsiveness to visual and somatosensory stimulation in Genetic Absence Epilepsy Rats from Strasbourg (GAERS), a well-established rat model for absence epilepsy. Animals were imaged under non-curarized awake state using a quiet, zero echo time, functional magnetic resonance imaging (fMRI) sequence. Sensory stimulations were applied during interictal and ictal periods. Whole-brain hemodynamic responses were compared between these two states. Additionally, a mean-field simulation model was used to explain the changes of neural responsiveness to visual stimulation between states. During a seizure, whole-brain responses to both sensory stimulations were suppressed and spatially hindered. In the cortex, hemodynamic responses were negatively polarized during seizures, despite the application of a stimulus. The mean-field simulation revealed restricted propagation of activity due to stimulation and agreed well with fMRI findings. Results suggest that sensory processing is hindered or even suppressed by the occurrence of an absence seizure, potentially contributing to decreased responsiveness during this absence epileptic process.


Assuntos
Encéfalo , Eletroencefalografia , Epilepsia Tipo Ausência , Imageamento por Ressonância Magnética , Animais , Ratos , Epilepsia Tipo Ausência/fisiopatologia , Encéfalo/fisiopatologia , Encéfalo/diagnóstico por imagem , Masculino , Vigília/fisiologia , Modelos Animais de Doenças , Convulsões/fisiopatologia , Estimulação Luminosa
20.
A A Pract ; 18(7): e01813, 2024 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-38975674

RESUMO

Incomplete neurological awakening manifested as aberrant patterns of electroencephalography (EEG) at emergence may be responsible for an unresponsive patient in the postanesthesia care unit (PACU). We describe a case of an individual who remained unresponsive but awake in the PACU. Retrospective, intraoperative EEG analysis showed low alpha power and a sudden shift from deep delta to arousal preextubation. We explored parallels with diminished motivation disorders and anesthesia-induced sleep paralysis due to imbalances in anesthetic drug sensitivity between brain regions. Our findings highlight the relevance of end-anesthesia EEG patterns in diagnosing delayed awakening.


Assuntos
Eletroencefalografia , Humanos , Período de Recuperação da Anestesia , Idoso , Masculino , Feminino , Idoso de 80 Anos ou mais
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