Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 19.145
Filtrar
1.
HardwareX ; 19: e00553, 2024 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-39099722

RESUMO

To continue sleep research activities during the lockdown resulting from the COVID-19 pandemic, experiments that were previously conducted in laboratories were shifted to the homes of volunteers. Furthermore, for extensive data collection, it is necessary to use a large number of portable devices. Hence, to achieve these objectives, we developed a low-cost and open-source portable monitor (PM) device capable of acquiring electroencephalographic (EEG) signals using the popular ESP32 microcontroller. The device operates based on instrumentation amplifiers. It also has a connectivity microcontroller with Wi-Fi and Bluetooth that can be used to stream EEG signals. This portable single-channel 3-electrode EEG device allowed us to record short naps and score different sleep stages, such as wakefulness, non rapid eye movement sleep (NREM), stage 1 (S1), stage 2 (S2), stage 3 (S3) and stage 4 (S4). We validated the device by comparing the obtained signals to those generated by a research-grade counterpart. The results showed a high level of accurate similarity between both devices, demonstrating the feasibility of using this approach for extensive and low-cost data collection of EEG sleep recordings.

2.
Front Comput Neurosci ; 18: 1416494, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-39099770

RESUMO

EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI). Currently, most researches focus on improving accuracy, while neglecting further research on the interpretability of models, we are committed to analyzing the impact of different brain regions and signal frequency bands on emotion generation based on graph structure. Therefore, this paper proposes a method named Dual Attention Mechanism Graph Convolutional Neural Network (DAMGCN). Specifically, we utilize graph convolutional neural networks to model the brain network as a graph to extract representative spatial features. Furthermore, we employ the self-attention mechanism of the Transformer model which allocates more electrode channel weights and signal frequency band weights to important brain regions and frequency bands. The visualization of attention mechanism clearly demonstrates the weight allocation learned by DAMGCN. During the performance evaluation of our model on the DEAP, SEED, and SEED-IV datasets, we achieved the best results on the SEED dataset, showing subject-dependent experiments' accuracy of 99.42% and subject-independent experiments' accuracy of 73.21%. The results are demonstrably superior to the accuracies of most existing models in the realm of EEG-based emotion recognition.

3.
Cureus ; 16(7): e63868, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-39099953

RESUMO

INTRODUCTION: The aim of our study is to assess the clinical manifestations, investigation results, and outcomes in Bulgarian patients with seizures in the course of COVID-19 infection. METHODS: We performed an open, prospective study during a 12-month period from January 2021 with the participation of 290 inpatients and outpatients with seizures who attended the Clinic of Neurology at the University Hospital in Plovdiv, Bulgaria. After a detailed anamnesis, they underwent neurological examination, EEG, neuroimaging, and lumbar puncture when needed. There was a prospective one-year follow-up regarding seizure frequency, EEG, and treatment. RESULTS: In 18 (5.9%) patients, seizures were related to COVID-19 infection. Nine (3.1%) patients had new-onset seizures, and in nine (3.1%) participants with epilepsy, there was a worsening of seizure frequency. New-onset seizures were more likely to occur in people above 65 years of age, within one to two months from the infection diagnosis. In one participant, seizures were related to fever. The most common seizure types were generalized tonic-clonic and focal motor seizures with/without loss of awareness. Antiseizure medications were started in seven participants. Viral encephalopathy was confirmed in two patients, one of them died. EEG showed focal epileptiform activity in four participants. The one-year prospective observation showed a favorable outcome in five patients who were without seizures, had normal EEG, and three were without treatment. Seizure frequency increase or seizure recurrence was typically observed for a short period of time in the epilepsy group. EEG was worsened in one patient and treatment changes were needed in five participants. CONCLUSION: In conclusion, our study results provide evidence about the progress and possible relationship between new-onset seizures and seizure worsening with COVID-19 infection.

4.
Comput Biol Med ; 180: 108951, 2024 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-39094326

RESUMO

Classifying individuals with neurological disorders and healthy subjects using EEG is a crucial area of research. The current feature extraction approach focuses on the frequency domain features in each of the EEG frequency bands and functional brain networks. In recent years, researchers have discovered and extensively studied stability differences in the electroencephalograms (EEG) of patients with neurological disorders. Based on this, this paper proposes a feature descriptor to characterize EEG instability. The proposed method starts by forming a signal point cloud through Phase Space Reconstruction (PSR). Subsequently, a pseudo-metric space is constructed, and pseudo-distances are calculated based on the consistent measure of the point cloud. Finally, Distance to Measure (DTM) Function are generated to replace the distance function in the original metric space. We calculated the relative distances in the point cloud by measuring signal similarity and, based on this, summarized the point cloud structures formed by EEG with different stabilities after PSR. This process demonstrated that Multivariate Kernel Density Estimation (MKDE) based on a Gaussian kernel can effectively separate the mappings of different stable components within the signal in the phase space. The two average DTM values are then proposed as feature descriptors for EEG instability.In the validation phase, the proposed feature descriptor is tested on three typical neurological disorders: epilepsy, Alzheimer's disease, and Parkinson's disease, using the Bonn dataset, CHB-MIT, the Florida State University dataset, and the Iowa State University dataset. DTM values are used as feature inputs for four different machine learning classifiers, and The results show that the best classification accuracy of the proposed method reaches 98.00 %, 96.25 %, 96.71 % and 95.34 % respectively, outperforming commonly used nonlinear descriptors. Finally, the proposed method is tested and analyzed using noisy signals, demonstrating its robustness compared to other methods.

5.
Clin EEG Neurosci ; : 15500594241264870, 2024 Aug 02.
Artigo em Inglês | MEDLINE | ID: mdl-39094550

RESUMO

Over the past decade, there has been extensive research on the mismatch negativity (MMN) and its promise as a biomarker of illness in people with schizophrenia (SZ). Nevertheless, when attempting to assess the early stages of illness progression, the utility of MMN has been inconsistent. Recently, researchers have been investigating a more advanced MMN paradigm (the complex MMN [cMMN]) which is believed to index higher-order cognitive processing and has been suggested to be a more effective indicator of the early phases of SZ. The cMMN is defined as a paradigm that relies on alterations within a pre-established pattern of stimuli. In this meta-analysis, we investigated cMMN deficits in individuals with SZ, including an analysis involving those in the first 5 years of illness. Our search also included individuals with bipolar disorder who experience psychosis; however, no related papers were found and thus, no findings are reported. Our findings indicate a small/moderate effect (d = 0.47), suggesting that individuals with SZ exhibit reduced cMMN amplitudes compared to individuals without SZ. Interestingly, this effect seems to be more pronounced in individuals within the first 5 years of their illness (d = 0.58), suggesting that cMMN might be a more sensitive biomarker in the early phases of SZ compared to traditional paradigms.

6.
J Neural Eng ; 2024 Aug 02.
Artigo em Inglês | MEDLINE | ID: mdl-39094617

RESUMO

OBJECTIVE: This study aims to address the challenges associated with data-driven electroencephalographic (EEG) data analysis by introducing a standardised library called BIDSAlign. This library efficiently processes and merges heterogeneous EEG datasets from different sources into a common standard template. The goal of this work is to create an environment that allows to preprocess public datasets in order to provide data for the effective training of deep learning architectures. Approach. The library can handle both BIDS (Brain Imaging Data Structure) and non-BIDS datasets, allowing the user to easily preprocess multiple public datasets. It unifies the EEG recordings acquired with different settings by defining a common pipeline and a specified channel template. An array of visualisation functions is provided inside the library, together with a user-friendly GUI to assist non-expert users throughout the workflow. Main results. BIDSAlign enables the effective use of public EEG datasets, providing valuable medical insights, even for non-experts in the field. Results from applying the library to datasets from OpenNeuro demonstrate its ability to extract significant medical knowledge through an end-to-end workflow, facilitating group analysis, visual comparison and statistical testing. Significance. BIDSAlign solves the lack of large EEG datasets by aligning multiple datasets to a standard template. This unlocks the potential of public EEG data for training deep learning models. It paves the way to promising contributions based on deep learning to clinical and non-clinical EEG research, offering insights that can inform neurological disease diagnosis and treatment strategies.

7.
Clin Perinatol ; 51(3): 573-586, 2024 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-39095097

RESUMO

Hypoxic-ischemic encephalopathy is the most common cause of neonatal seizures. Continuous electroencephalographic monitoring is recommended given high rates of subclinical seizures. Prompt diagnosis and treatment of seizures may improve neurodevelopmental outcomes. International League Against Epilepsy guidelines indicate that (1) phenobarbital remains the first-line treatment of neonatal seizures and (2) early discontinuation of antiseizure medications following resolution of acute provoked seizures, and prior to discharge home, is recommended. Long-term follow-up of these infants is necessary to screen for postneonatal epilepsy and support neurodevelopment.


Assuntos
Anticonvulsivantes , Eletroencefalografia , Hipóxia-Isquemia Encefálica , Fenobarbital , Convulsões , Humanos , Hipóxia-Isquemia Encefálica/terapia , Recém-Nascido , Anticonvulsivantes/uso terapêutico , Convulsões/tratamento farmacológico , Fenobarbital/uso terapêutico
8.
Clin Perinatol ; 51(3): 649-663, 2024 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-39095102

RESUMO

Electroencephalography (EEG) is a key objective biomarker of newborn brain function, delivering critical, cotside insights to aid the management of encephalopathy. Access to continuous EEG is limited, forcing reliance on subjective clinical assessments. In hypoxia ischaemia, the primary cause of encephalopathy, alterations in EEG patterns correlate with. injury severity and evolution. As HIE evolves, causing secondary neuronal death, EEG can track injury progression, informing neuroprotective strategies, seizure management and prognosis. Despite its value, challenges with interpretation and lack of on site expertise has limited its broader adoption. Technological advances, particularly in digital EEG and machine learning, are enhancing real-time analysis. This will allow EEG to expand its role in HIE diagnosis, management and outcome prediction.


Assuntos
Biomarcadores , Eletroencefalografia , Hipóxia-Isquemia Encefálica , Humanos , Hipóxia-Isquemia Encefálica/fisiopatologia , Hipóxia-Isquemia Encefálica/diagnóstico , Eletroencefalografia/métodos , Recém-Nascido , Biomarcadores/metabolismo , Prognóstico , Aprendizado de Máquina , Encéfalo/fisiopatologia
9.
Eur J Pediatr ; 2024 Aug 07.
Artigo em Inglês | MEDLINE | ID: mdl-39110215

RESUMO

Preterm birth represents a public health problem, with prematurity being the leading cause of infant mortality. An objective brain maturation and oxygenation measurement are necessary. The objective has been To test the feasibility of EEG and NIRS combination in the assessment of physiological brain maturation and oxygenation in preterm and non-preterm babies. A systematic review in Pubmed, Web of Science, MEDLINE, Cochrane, Dialnet, CINAHL, Scopus, Lilacs and PEDro databases until December 2022 was developed. 598 registers were found, finally 5 of them reached the inclusion criteria. Two independent reviewers analyzed data and a third reviewer were available for discrepancies. All articles combined EEG and NIRS to assess brain oxygenation and maturation in healthy new-born babies. There is an agreement on the electrode's placement for EEG at P3 and P4; besides, these regions have been shown to be a development predictive area, as well as the frontoparietal region for the NIRS region and comparison between regions. There is little evidence about the physiological brain electrical activity and oxygenation without stimuli.Conclusion: EEG and NIRS have been useful to assess brain electrical activity and oxygenation in preterm and non-preterm. The combined measurement of these instruments could be essential in neurological disorders diagnosis or their sequels. Unfortunately, the heterogeneity of the results found prevents a consensus on which variables are the most appropriate for the assessment of this population. What is Known • Brain assessment could help clinicians to prevent sequels. • There is an agreement for EEG electrodes placement at P3 and P4 region. What is New • EEG and NIRS assessment are effective measurements for preterm babies. • P3 and P4 regions have shown to be a predictive area of development, as well as the frontoparietal region for NIRS assessment.

10.
EBioMedicine ; 106: 105259, 2024 Aug 05.
Artigo em Inglês | MEDLINE | ID: mdl-39106531

RESUMO

BACKGROUND: Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnessing the wealth of complex EEG signals to isolate relevant brain activity. Yet, ML studies in EEG tend to ignore physiological artefacts, which may cause problems for deriving biomarkers specific to the central nervous system (CNS). METHODS: We present a framework for conceptualising machine learning from CNS versus peripheral signals measured with EEG. A signal representation based on Morlet wavelets allowed us to define traditional brain activity features (e.g. log power) and alternative inputs used by state-of-the-art ML approaches based on covariance matrices. Using more than 2600 EEG recordings from large public databases (TUAB, TDBRAIN), we studied the impact of peripheral signals and artefact removal techniques on ML models in age and sex prediction analyses. FINDINGS: Across benchmarks, basic artefact rejection improved model performance, whereas further removal of peripheral signals using ICA decreased performance. Our analyses revealed that peripheral signals enable age and sex prediction. However, they explained only a fraction of the performance provided by brain signals. INTERPRETATION: We show that brain signals and body signals, both present in the EEG, allow for prediction of personal characteristics. While these results may depend on specific applications, our work suggests that great care is needed to separate these signals when the goal is to develop CNS-specific biomarkers using ML. FUNDING: All authors have been working for F. Hoffmann-La Roche Ltd.

11.
Artigo em Inglês | MEDLINE | ID: mdl-39086252

RESUMO

Estimation of mental workload from electroencephalogram (EEG) signals aims to accurately measure the cognitive demands placed on an individual during multitasking mental activities. By analyzing the brain activity of the subject, we can determine the level of mental effort required to perform a task and optimize the workload to prevent cognitive overload or underload. This information can be used to enhance performance and productivity in various fields such as healthcare, education, and aviation. In this paper, we propose a method that uses EEG and deep neural networks to estimate the mental workload of human subjects during multitasking mental activities. Notably, our proposed method employs subject-independent classification. We use the "STEW" dataset, which consists of two tasks, namely "No task" and "simultaneous capacity (SIMKAP)-based multitasking activity". We estimate the different workload levels of two tasks using a composite framework consisting of brain connectivity and deep neural networks. After the initial preprocessing of EEG signals, an analysis of the relationships between the 14 EEG channels is conducted to evaluate effective brain connectivity. This assessment illustrates the information flow between various brain regions, utilizing the direct Directed Transfer Function (dDTF) method. Then, we propose a deep hybrid model based on pre-trained Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for the classification of workload levels. The accuracy of the proposed deep model achieved 83.12% according to the subject-independent leave-subject-out (LSO) approach. The pre-trained CNN + LSTM approaches to EEG data have been found to be an accurate method for assessing the mental workload.

12.
Eur J Neurol ; : e16424, 2024 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-39087560

RESUMO

BACKGROUND AND PURPOSE: Precise and timely diagnosis is crucial for the optimal use of emerging disease-modifying treatments for Alzheimer disease (AD). Electroencephalography (EEG), which is noninvasive and cost-effective, can capture neural abnormalities linked to various dementias. This study explores the use of individual alpha frequency (IAF) derived from EEG as a diagnostic and prognostic tool in cognitively impaired patients. METHODS: This retrospective study included 375 patients from the tertiary Memory Clinic of IRCCS San Raffaele Hospital, Milan, Italy. Participants underwent clinical and neuropsychological assessments, brain imaging, cerebrospinal fluid biomarker analysis, and resting-state EEG. Patients were categorized by amyloid status, the AT(N) classification system, clinical diagnosis, and mild cognitive impairment (MCI) progression to AD dementia. IAF was calculated and compared among study groups. Receiver operating characteristic (ROC) analysis was used to calculate its discriminative performance. RESULTS: IAF was higher in amyloid-negative subjects and varied significantly across AT(N) groups. ROC analysis confirmed IAF's ability to distinguish A-T-N- from the A+T+N+ and A+T-N+ groups. IAF was lower in AD and Lewy body dementia patients compared to MCI and other dementia types, with moderate discriminatory capability. Among A+ MCI patients, IAF was significantly lower in those who converted to AD within 2 years compared to stable MCI patients and predicted time to conversion (p < 0.001, R = 0.38). CONCLUSIONS: IAF is a valuable tool for dementia diagnosis and prognosis, correlating with amyloid status and neurodegeneration. It effectively predicts MCI progression to AD, supporting its use in early, targeted interventions in the context of disease-modifying treatments.

13.
Front Hum Neurosci ; 18: 1390714, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-39086374

RESUMO

Error-related potentials (ErrPs) are brain signals known to be generated as a reaction to erroneous events. Several works have shown that not only self-made errors but also mistakes generated by external agents can elicit such event-related potentials. The possibility of reliably measuring ErrPs through non-invasive techniques has increased the interest in the brain-computer interface (BCI) community in using such signals to improve performance, for example, by performing error correction. Extensive calibration sessions are typically necessary to gather sufficient trials for training subject-specific ErrP classifiers. This procedure is not only time-consuming but also boresome for participants. In this paper, we explore the effectiveness of ErrPs in closed-loop systems, emphasizing their dependency on precise single-trial classification. To guarantee the presence of an ErrPs signal in the data we employ and to ensure that the parameters defining ErrPs are systematically varied, we utilize the open-source toolbox SEREEGA for data simulation. We generated training instances and evaluated the performance of the generic classifier on both simulated and real-world datasets, proposing a promising alternative to conventional calibration techniques. Results show that a generic support vector machine classifier reaches balanced accuracies of 72.9%, 62.7%, 71.0%, and 70.8% on each validation dataset. While performing similarly to a leave-one-subject-out approach for error class detection, the proposed classifier shows promising generalization across different datasets and subjects without further adaptation. Moreover, by utilizing SEREEGA, we can systematically adjust parameters to accommodate the variability in the ErrP, facilitating the systematic validation of closed-loop setups. Furthermore, our objective is to develop a universal ErrP classifier that captures the signal's variability, enabling it to determine the presence or absence of an ErrP in real EEG data.

15.
Brain Commun ; 6(4): fcae227, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-39086629

RESUMO

Electrophysiologic disturbances due to neurodegenerative disorders such as Alzheimer's disease and Lewy Body disease are detectable by scalp EEG and can serve as a functional measure of disease severity. Traditional quantitative methods of EEG analysis often require an a-priori selection of clinically meaningful EEG features and are susceptible to bias, limiting the clinical utility of routine EEGs in the diagnosis and management of neurodegenerative disorders. We present a data-driven tensor decomposition approach to extract the top 6 spectral and spatial features representing commonly known sources of EEG activity during eyes-closed wakefulness. As part of their neurologic evaluation at Mayo Clinic, 11 001 patients underwent 12 176 routine, standard 10-20 scalp EEG studies. From these raw EEGs, we developed an algorithm based on posterior alpha activity and eye movement to automatically select awake-eyes-closed epochs and estimated average spectral power density (SPD) between 1 and 45 Hz for each channel. We then created a three-dimensional (3D) tensor (record × channel × frequency) and applied a canonical polyadic decomposition to extract the top six factors. We further identified an independent cohort of patients meeting consensus criteria for mild cognitive impairment (30) or dementia (39) due to Alzheimer's disease and dementia with Lewy Bodies (31) and similarly aged cognitively normal controls (36). We evaluated the ability of the six factors in differentiating these subgroups using a Naïve Bayes classification approach and assessed for linear associations between factor loadings and Kokmen short test of mental status scores, fluorodeoxyglucose (FDG) PET uptake ratios and CSF Alzheimer's Disease biomarker measures. Factors represented biologically meaningful brain activities including posterior alpha rhythm, anterior delta/theta rhythms and centroparietal beta, which correlated with patient age and EEG dysrhythmia grade. These factors were also able to distinguish patients from controls with a moderate to high degree of accuracy (Area Under the Curve (AUC) 0.59-0.91) and Alzheimer's disease dementia from dementia with Lewy Bodies (AUC 0.61). Furthermore, relevant EEG features correlated with cognitive test performance, PET metabolism and CSF AB42 measures in the Alzheimer's subgroup. This study demonstrates that data-driven approaches can extract biologically meaningful features from population-level clinical EEGs without artefact rejection or a-priori selection of channels or frequency bands. With continued development, such data-driven methods may improve the clinical utility of EEG in memory care by assisting in early identification of mild cognitive impairment and differentiating between different neurodegenerative causes of cognitive impairment.

16.
Brain Dev ; 2024 Jul 31.
Artigo em Inglês | MEDLINE | ID: mdl-39089917

RESUMO

BACKGROUND: Acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) develops along with status epilepticus and widespread subcortical white matter edema. We aimed to evaluate the epileptic foci and networks in two patients with epilepsy after AESD using simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI). METHODS: Statistically significant blood oxygen level-dependent (BOLD) responses related to interictal epileptiform discharges (IEDs) were analyzed using an event-related design of hemodynamic response functions with multiple peaks. RESULTS: Patient 1 developed focal seizures at age 10 years, one year after AESD onset. Positive BOLD changes were observed in the bilateral frontotemporal lobes, left parietal lobe, and left insula. BOLD changes were also observed in the subcortical structures. Patient 2 developed epileptic spasms at age two years, one month after AESD onset. Following total corpus callosotomy (CC) at age three years, the epileptic spasms resolved, and neurodevelopmental improvement was observed. Before CC, positive BOLD changes were observed bilaterally in the frontotemporal lobes. BOLD changes were also observed in the subcortical structures. After CC, the positive BOLD changes were localized in the temporal lobe ipsilateral to the IEDs, and the negative BOLD changes were mainly in the cortex and subcortical structures of the hemisphere ipsilateral to IEDs. CONCLUSION: EEG-fMRI revealed multiple epileptic foci and extensive epileptic networks, including subcortical structures in two cases with post-AESD epilepsy. CC may be effective in disconnecting the bilaterally synchronous epileptic networks of epileptic spasms after AESD, and pre-and post-operative changes in EEG-fMRI may reflect improvements in epileptic symptoms.

17.
Pak J Med Sci ; 40(7): 1397-1402, 2024 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-39092067

RESUMO

Objective: To investigate whether Infra-low frequency Neurofeedback (ILF-NFB) training can improve brain electrical activity in children with autism spectrum disorders ASD. Method: This single arm pre and post intervention study was carried out at IBMS (Institute of Basic Medical Sciences), Khyber Medical University, Peshawar and Shaheed Zulfiqar Ali Bhutto Medical University (SZABMU), Islamabad from January 2021 to December 2022. A purposive sampling technique was used. Thirty-five ASD children (male=24; female=11; 7-17 years) were provided with 30 sessions of infra low frequency (ILF) neurofeedback training for 15-20 minutes, during 10 weeks. Childhood Autism Rating Scale (CARS) scoring was done and electroencephalogram (EEG) activity was compared before and after ILF-NF training sessions. Results: Around 62.9% participants had mild-moderate autism and 37.1% had severe autism. Wilcoxon Signed rank test revealed a significant decline in delta (Pre-test=47.31±19.22, Post-test=22.07±6.83; p=<0.001), theta (Pre-test=24.75±16.62, Post-test=12.37±3.59; p=<0.001) and alpha (Pre-test=12.01±9.81, Post-test=4.03±1.61; p=< 0.001) waves. Mann Whitney U test exhibited no significant gender differences in EEG pattern before and after neurofeedback except in theta waves (p=0.03) before the intervention. Conclusion: Decline in delta, theta, beta and alpha waves propose that ILF-NF training can be effective in improving the EEG activity. ILF-NFB can be perceived as a valuable non-invasive, non-pharmacological intervention for improving EEG pattern via reintegration of brain activity resulting in increased the attention and focus, enhanced mental stability and cognitive engagement.

18.
Artigo em Inglês | MEDLINE | ID: mdl-39096235

RESUMO

BACKGROUND: "Metacontrol" describes the ability to maintain an optimal balance between cognitive control styles that are either more persistent or more flexible. Recent studies have shown a link between metacontrol and aperiodic EEG patterns. The present study aimed to gain more insight into the neurobiological underpinnings of metacontrol by using Methylphenidate (MPH), a compound known to increase postsynaptic catecholamine levels and to modulate cortical noise. METHODS: In a double-blind, randomized, placebo-controlled study design, we investigated the effect of methylphenidate (0.5 mg/kg) on aperiodic EEG activity during a flanker task in a sample of n = 25 neurotypical adults. To quantify cortical noise, we employed the FOOOF (fitting oscillations & one over f) algorithm. RESULTS: Compared to placebo, MPH increased the aperiodic exponent, suggesting that it reduces cortical noise in two ways: First, it did so in a state-like fashion, as the main effect of the drug was visible and significant in both pre-trial and within-trial periods. Second, the electrode-specific analyses showed that the drug also affects specific processes by dampening the downregulation of noise in conditions requiring more control. CONCLUSIONS: Our findings suggest that the aperiodic exponent provides a neural marker of metacontrol states and changes therein. Further, we propose that the effectiveness of medications targeting catecholaminergic signaling can be evaluated by studying changes of cortical noise; fostering the idea of using the quantification of cortical noise as an indicator in pharmacological treatment.

19.
Artigo em Inglês | MEDLINE | ID: mdl-39096513

RESUMO

Recent studies using resting-state functional magnetic resonance imaging have shown that loneliness is associated with altered blood oxygenation in several brain regions. However, the relationship between loneliness and changes in neuronal rhythm activity in the brain remains unclear. To evaluate brain rhythm, we conducted an exploratory resting-state electroencephalogram (EEG) study of loneliness. We recorded resting-state EEG signals from 139 participants (94 women; mean age = 19.96 years) and analyzed power spectrum density (PSD) and functional connectivity (FC) in both the electrode and source spaces. The PSD analysis revealed significant correlations between loneliness scores and decreased beta-band powers, which may indicate negative emotion, attention, reward, and/or sensorimotor processing. The FC analysis revealed a trend of alpha-band FC associated with individuals' loneliness scores. These findings provide new insights into the neural basis of loneliness, which will facilitate the development of neurobiologically informed interventions for loneliness.

20.
Neural Netw ; 179: 106580, 2024 Jul 26.
Artigo em Inglês | MEDLINE | ID: mdl-39096751

RESUMO

Auditory Attention Detection (AAD) aims to detect the target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional convolutional neural networks designed for processing Euclidean data like images. This makes it challenging to handle EEG signals, which possess non-Euclidean characteristics. In order to address this problem, this paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input. Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals. In addition, to further improve AAD detection performance, self-distillation, consisting of feature distillation and hierarchical distillation strategies at each layer, is integrated. These strategies leverage features and classification results from the deepest network layers to guide the learning of shallow layers. Our experiments are conducted on two publicly available datasets, KUL and DTU. Under a 1-second time window, we achieve results of 90.0% and 79.6% accuracy on KUL and DTU, respectively. We compare our DGSD method with competitive baselines, and the experimental results indicate that the detection performance of our proposed DGSD method is not only superior to the best reproducible baseline but also significantly reduces the number of trainable parameters by approximately 100 times.

SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA