EEG Effects Research Articles

Research on the Exoskeleton Enhancing Human Touch

The integration of brain-computer interfaces (BCIs) with exoskeleton systems offers a unique potential to enhance and restore human sensory and motor capabilities. This review delves into both invasive and non-invasive BCI technologies, with a special emphasis on the practical application of electroencephalography (EEG). It critically assess the effectiveness and limitations of EEG in controlling exoskeletons, while providing a detailed comparison of various control methods, including direct neuromuscular stimulation, neurofeedback, and machine learning-based intelligent strategies. Additionally, this review addresses the technical challenges faced by integrated systems, particularly in performing complex tasks and delivering real-time feedback, such as the intricacies of signal decoding, system stability, and user adaptability. The conclusion underscores the importance of future research in enhancing system reliability and accuracy, refining user interfaces, and developing novel algorithms to improve performance and user experience. This review aims to equip researchers in the field with a robust theoretical framework and practical insights, facilitating further advancements in the synergy between BCI and exoskeleton technologies.

The effect of mobile phone electromagnetic fields on the human resting state wake EEG and event-related potential: A systematic review and meta-analysis.

The rapid growth of mobile phone usage and its use of radiofrequency electromagnetic fields (RF-EMF) have raised concerns about potential health risks. Researchers have conducted studies to examine the effects of RF-EMF on the brain using electroencephalography (EEG). We conducted a systematic quality assessment and meta-analysis of published research in this field to establish high-quality studies as references for future protocols. The electronic search yielded 244 records from which a total of 51 studies were included in the review after excluding studies based on study design, and data or report availability. Of these 51 studies, 31 (61%) focused on resting state wake EEG and 20 (39%) on event-related potentials (ERP). None of the 51 studies were free from risk of bias. From the 51 included studies, we were able to use seven studies to create three different groups for meta-analysis for resting state wake EEG and five studies to create 10 different groups for meta-analysis for ERP. Per group the number of studies varies from 1 to 5. Our procedure is the first systematic quality assessment in this field and revealed three important findings. First, there is evidence of an effect on the EEG of a 2G protocol using an eyes-open condition. Second, we did not find evidence for EEG effects during task performance. This suggests that the impact of EMF during task performance is less pronounced compared to the resting state condition. Third, this meta-analysis shows that the field is unable to create an evidence base for most comparisons due to heterogeneity. We therefore advise that all future studies are double-blind in nature, adhere to the methodological standard of randomized experiments, and publish their protocols first.

Power spectral density-based resting-state EEG classification of first-episode psychosis

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.

Transcranial direct current stimulation over medial prefrontal cortex reduced alpha power and functional connectivity during somatic but not semantic self-referential processing

Past self-report and cognitive-behavioural studies of the effects of transcranial direct current stimulation (tDCS) targeting the medial prefrontal cortex (mPFC) on semantic self-referential processing (SRP) have yielded mixed results. Meanwhile, electroencephalography (EEG) studies show that alpha oscillation (8–12 Hz) may be involved during both semantic and somatic SRP, although the effect of tDCS on alpha-EEG during SRP remains unknown. The current study assessed the EEG and subjective effects of 2 mA tDCS over the mPFC while participants were SRP either on semantic (life roles, e.g., “friend”) or somatic (outer body, e.g., “arms”) self-referential stimuli compared to resting state and an external attention memory task in 52 young adults. Results showed that whereas mPFC-tDCS did not yield significant changes in participants’ mood or experienced attention or pleasantness levels during the SRP task, EEG source analysis indicated, compared to sham stimulation, that tDCS reduced alpha power during somatic but not semantic SRP in the posterior cingulate cortex (PCC), and the frontal, parietal, temporal, and somatosensory cortex, and reduced the functional connectivity between the left inferior parietal lobule and the ventral PCC, but only when mPFC-tDCS was applied at the second while not the first experimental session. Our results suggest that while mPFC-tDCS may be insufficient to alter immediate subjective experience during SRP, mPFC-tDCS may modulate the power and functional connectivity of the brain’s alpha oscillations during somatic SRP. Future research directions are discussed.

해양치유 프로그램을 통한 암환자와 고창 주민의 뇌파 및 정신건강 증진 효과 비교

해양치유 프로그램을 통한 암환자와 고창 주민의 뇌파 및 정신건강 증진 효과 비교

The Role of Routine Electroencephalography in the Diagnosis of Seizures in Medical Intensive Care Units.

Seizures should be diagnosed and treated to ensure optimal health outcomes in critically ill patients admitted in the medical intensive care unit (MICU). Continuous electroencephalography is still infrequently used in the MICU. We investigated the effectiveness of routine EEG (rEEG) in detecting seizures in the MICU. A total of 560 patients admitted to the MICU between October 2018 and March 2023 and who underwent rEEG were reviewed. Seizure-related rEEG constituted 47% of all rEEG studies. Totally, 39% of the patients experienced clinical seizures during hospitalization; among them, 48% experienced the seizure, and 13% experienced their first seizure after undergoing an rEEG study. Seventy-seven percent of the patients had unfavorable short-term outcomes. Patients with cardiovascular diseases were the most likely to have the suppression/burst suppression (SBS) EEG pattern and the highest mortality rate. The rhythmic and periodic patterns (RPPs) and electrographic seizure (ESz) EEG pattern were associated with seizures within 24 h after rEEG, which was also related to unfavorable outcomes. Significant predictors of death were age > 59 years, the male gender, the presence of cardiovascular disease, a Glasgow Coma Scale score ≤ 5, and the SBS EEG pattern, with a predictive performance of 0.737 for death. rEEG can help identify patients at higher risk of seizures. We recommend repeated rEEG in patients with ESz or RPP EEG patterns to enable a more effective monitoring of seizure activities.

Two are better than one: Differences in cortical EEG patterns during auditory and visual verbal working memory processing between Unilateral and Bilateral Cochlear Implanted children

Despite the proven effectiveness of cochlear implant (CI) in the hearing restoration of deaf or hard-of-hearing (DHH) children, to date, extreme variability in verbal working memory (VWM) abilities is observed in both unilateral and bilateral CI user children (CIs). Although clinical experience has long observed deficits in this fundamental executive function in CIs, the cause to date is still unknown. Here, we have set out to investigate differences in brain functioning regarding the impact of monaural and binaural listening in CIs compared with normal hearing (NH) peers during a three-level difficulty n-back task undertaken in two sensory modalities (auditory and visual). The objective of this pioneering study was to identify electroencephalographic (EEG) marker pattern differences in visual and auditory VWM performances in CIs compared to NH peers and possible differences between unilateral cochlear implant (UCI) and bilateral cochlear implant (BCI) users. The main results revealed differences in theta and gamma EEG bands. Compared with hearing controls and BCIs, UCIs showed hypoactivation of theta in the frontal area during the most complex condition of the auditory task and a correlation of the same activation with VWM performance. Hypoactivation in theta was also observed, again for UCIs, in the left hemisphere when compared to BCIs and in the gamma band in UCIs compared to both BCIs and NHs. For the latter two, a correlation was found between left hemispheric gamma oscillation and performance in the audio task. These findings, discussed in the light of recent research, suggest that unilateral CI is deficient in supporting auditory VWM in DHH. At the same time, bilateral CI would allow the DHH child to approach the VWM benchmark for NH children. The present study suggests the possible effectiveness of EEG in supporting, through a targeted approach, the diagnosis and rehabilitation of VWM in DHH children.

Characterization of Vagus Nerve Stimulation (VNS) Dose-Dependent Effects on EEG Power Spectrum and Synchronization.

This study investigates the dose-dependent EEG effects of Vagus Nerve Stimulation (VNS) in patients with drug-resistant epilepsy. This research examines how varying VNS intensities impacts EEG power spectrum and synchronization in a cohort of 28 patients. Patients were categorized into responders, partial-responders, and non-responders based on seizure frequency reduction. The methods involved EEG recordings at incremental VNS intensities, followed by spectral and synchronization analysis. The results reveal significant changes in EEG power, particularly in the delta and beta bands across different intensities. Notably, responders exhibited distinct EEG changes compared to non-responders. Our study has found that VNS intensity significantly influences EEG power topographic allocation and brain desynchronization, suggesting the potential use of acute dose-dependent effects to personalized VNS therapy in the treatment of epilepsy. The findings underscore the importance of individualized VNS dosing for optimizing therapeutic outcomes and highlight the use of EEG metrics as an effective tool for monitoring and adjusting VNS parameters. These insights offer a new avenue for developing individualized VNS therapy strategies, enhancing treatment efficacy in epilepsy.

The influence of EEG-biofeedback training and Beta waves in normoxia and normobaric hypoxia on the bench press in judo athletes

The aim of this study was to assess the effect of EEG -Biofeedback training in normoxic and normobaric hypoxic conditions on bench press improving in elite judo athletes. The study included 20 male elite judo athletes. Each athlete was a member of the Polish National Team and held either an International Master Class (MM) or National Master Class (M) – purposive selection. The research was carried out over two cycles that differed in the external conditions of EEG biofeedback trainings for both the control and experimental groups. The experimental group underwent training under simulated hypoxic conditions The training sessions for the experimental group were conducted at a simulated altitude of 3000m above sea level. Each cycle of the study comprised of 15 training sessions. The control group's study followed the same frequency, and duration of EEG biofeedback training sessions, and demonstrated an identical pattern to that of the experimental group, but in normoxia conditions. In the discussed cycle conducted in hypoxia, a clear upward dynamics of Beta wave values with a growing trend from session 14 was observed. In normoxia, the variability in the EG group had a significantly less dynamic course. In hypoxia, a clear upward dynamics of the achieved PP values with a growing trend from measurement 14 was observed. In normoxia, the discussed measurement cycle conducted in the CG group showed a lesser upward dynamics of achieved PP values with a growing trend from measurement 10. The overall conclusion from these analyses suggests that EEG biofeedback training in hypoxic conditions may have a beneficial impact on brain activity and improvement in muscle strength and power. These results may be of significant importance for sports training and rehabilitation, especially for individuals engaged in sports disciplines requiring physical exertion in low oxygen environments.

A Few-Shot Transfer Learning Approach for Motion Intention Decoding from Electroencephalographic Signals.

In this study, a few-shot transfer learning approach was introduced to decode movement intention from electroencephalographic (EEG) signals, allowing to recognize new tasks with minimal adaptation. To this end, a dataset of EEG signals recorded during the preparation of complex sub-movements was created from a publicly available data collection. The dataset was divided into two parts: the source domain dataset (including 5 classes) and the support (target domain) dataset, (including 2 classes) with no overlap between the two datasets in terms of classes. The proposed methodology consists in projecting EEG signals into the space-frequency-time domain, in processing such projections (rearranged in channels × frequency frames) by means of a custom EEG-based deep neural network (denoted as EEGframeNET5), and then adapting the system to recognize new tasks through a few-shot transfer learning approach. The proposed method achieved an average accuracy of 72.45±4.19% in the 5-way classification of samples from the source domain dataset, outperforming comparable studies in the literature. In the second phase of the study, a few-shot transfer learning approach was proposed to adapt the neural system and make it able to recognize new tasks in the support dataset. The results demonstrated the system's ability to adapt and recognize new tasks with an average accuracy of 80±0.12% in discriminating hand opening/closing preparation and outperforming reported results in the literature. This study suggests the effectiveness of EEG in capturing information related to the motor preparation of complex movements, potentially paving the way for BCI systems based on motion planning decoding. The proposed methodology could be straightforwardly extended to advanced EEG signal processing in other scenarios, such as motor imagery or neural disorder classification.

EEG‐based neural biomarker for diagnosis and assessment of mild cognitive impairment and Alzheimer’s disease

AbstractBackgroundElectroencephalogram (EEG) has emerged as a non‐invasive tool to detect the aberrant neuronal activity related to different stages of Alzheimer’s disease (AD). However, the effectiveness of EEG in the precise diagnosis and prediction of AD and its preclinical stage, mild cognitive impairment (MCI), has yet to be fully elucidated. In this study, we aimed to identify key EEG biomarkers that are effective in distinguishing patients at the early stage of AD and monitoring the progression of AD.MethodA total of 890 participants, including 189 patients with MCI, 330 patients with AD, 125 patients with other dementias (frontotemporal dementia, dementia with Lewy bodies, and vascular cognitive impairment), and 246 healthy controls (HC) were enrolled. Biomarkers were extracted from resting‐state EEG recordings for a three‐level classification of HC, MCI, and AD. The optimal EEG biomarkers were then identified based on the classification performance. Random forest regression was used to train a series of predictive models by combining participants’ EEG biomarkers, demographic information (i.e., sex, age), CSF biomarkers, and APOE phenotype for predicting the disease progression and individual’s cognitive function.ResultThe identified EEG biomarkers achieved over 70% accuracy in the three‐level classification of HC, MCI, and AD. Among all six groups, the most prominent effects of AD‐linked neurodegeneration on EEG metrics were localized at parieto‐occipital regions. Noteworthy, the absolute theta power of channel O2 was negatively correlated with Aβ42 (r = ‐0.358, p < 0.001) and positively correlated with p‐tau (r = 0.442, p < 0.001). In terms of cognitive assessment, the Hjorth mobility of channel O1, O2 and P4 were found to be positively associated with MMSE and MoCA scores (r = 0.416‐0.464, all p < 0.001). In the predictive analyses, the optimal EEG features were more effective than the CSF + APOE biomarkers in predicting the age of onset and disease course, whereas the combination of EEG + CSF + APOE measures achieved the best performance for all targets of prediction.ConclusionOur study indicates that EEG can be used as a useful screening tool for the diagnosis and disease progression evaluation of MCI and AD.

The effect of EEG and fNIRS in the digital assessment and digital therapy of Alzheimer’s disease: a systematic review

In the context of population aging, the growing problem of Alzheimer’s disease (AD) poses a great challenge to mankind. Although there has been considerable progress in exploring the etiology of AD, i.e., the important role of amyloid plaques and neurofibrillary tangles in the progression of AD has been widely accepted by the scientific community, traditional treatment and monitoring modalities have significant limitations. Therefore novel evaluation and treatment modalities for Alzheimer’s disease are called for emergence. In this research, we sought to review the effectiveness of digital treatment based on monitoring using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG). This work searched four electronic databases using a keyword approach and focused on journals focusing on AD and geriatric cognition. Finally, 21 articles were included. The progress of digital therapy and outcome monitoring in AD was reviewed, including digital therapy approaches on different platforms and different neuromonitoring techniques. Because biomarkers such as theta coherence, alpha and beta rhythms, and oxyhemoglobin are effective in monitoring the cognitive level of AD patients, and thus the efficacy of digital therapies, this review particularly focuses on the biomarker validation results of digital therapies. The results show that digital treatment based on biomarker monitoring has good effectiveness. And the effectiveness is reflected in the numerical changes of biomarker indicators monitored by EEG and fNIRS before and after digital treatment. Increases or decreases in the values of these indicators collectively point to improvements in cognitive function (mostly moderate to large effect sizes). The study is the first to examine the state of digital therapy in AD from the perspective of multimodal monitoring, which broadens the research perspective on the effectiveness of AD and gives clinical therapists a “reference list” of treatment options. They can select a specific protocol from this “reference list” in order to tailor digital therapy to the needs of individual patients.

Combined study of transcranial direct current stimulation and electroencephalography for the analysis on upper limb function: a systematic review

Neurological disorders can directly affect basic functions of the upper limbs, leading to an alteration in or the loss of motor capacity and hindering the performance of activities of daily living. Transcranial direct current stimulation (tDCS) is a noninvasive technique capable of modulating cortical excitability, favoring both brain activity and neuroplasticity. The objective was to systematize and discuss the effects of transcranial direct current stimulation combined with electroencephalogram for the analysis of upper limb movements in individuals with neurological impairment. For such, the following research question was used: Is the combined use of tDCS and EEG effective for the analysis of upper limb movements? A systematic review was performed in the international network of databanks using the following criteria: (1) design: randomized controlled clinical trial; (2) population: healthy individuals or individuals with neurological impairment; (3) intervention: tDCS combined with EEG; (4) control group with different intervention or no intervention; (5) outcome: analysis of upper limb movement and function. Eight arti-cles were selected, revealing no consensus on the parameters to use. Few studies have evaluated the use of tDCS, physical therapy and electroencephalogram. The small number of studies and methodological variation hinder the determination of the combined effects of tDCS and EEG. Further studies are needed to define the ideal quantity of session, intervals between sessions and the administration of physical therapy concomitant to the use of tDCS.

Machine and human agents in moral dilemmas: automation–autonomic and EEG effect

Machine and human agents in moral dilemmas: automation–autonomic and EEG effect

Cognitive Workload of Novice Forklift Truck Drivers in VR-based Training

There is increasing use of Virtual Reality (VR) to train forklift truck operators but a lack of sufficient understanding of how cognitive workload changes with respect to different task demands in VR-based training. In this study, 19 novice participants completed three forklift driving lessons with varying difficulty levels (low, medium, and high) using a VR simulator. To examine the effect of repeated training on cognitive workload, two sessions were repeated by participants using the same procedures. Cognitive workload was assessed with objective (electroencephalogram [EEG] activity) and subjective (NASA-TLX) measurements. EEG theta power and NASA-TLX (mental workload) scores were significantly higher for high than low difficulty levels. However, both EEG and NASA-TLX responses were reduced with repeated training in the second session. These findings highlight the effectiveness of EEG in continuous monitoring of workload variation caused by task difficulty and implementing training programs to moderate cognitive workload for forklift operators.

Evaluation of Brain Source Localization Methods Based on Test-Retest Reliability With Multiple Session EEG Data.

Various EEG source localization methods have been proposed for functional brain research. The evaluation and comparison of these methods are usually based on simulated data but not real EEG data, as the ground truth of source localization is unknown. In this study, we aim to evaluate source localization methods quantitatively under the real situation. We examined the test-retest reliability of the source signals reconstructed from a public six-session EEG data of 16 subjects performing face recognition tasks by five mainstream methods, including weighted minimum norm estimation (WMN), dynamical Statistical Parametric Mapping (dSPM), Standardized LOw Resolution brain Electromagnetic TomogrAphy (sLORETA), dipole modeling and linearly constrained minimum variance (LCMV) beamformers. All methods were evaluated in terms of peak localization reliability and amplitude reliability of source signals. In the two brain regions responsible for static face recognition, all methods have promising peak localization reliability, with WMN showing the smallest peak dipole distance between session pairs. The spatial stability of source localization in the familiar face condition is better than those in the unfamiliar face and the scrambled face conditions in the face recognition areas in the right hemisphere. In addition, the test-retest reliability of source amplitude by all methods is good to excellent under the familiar face condition. Stable and reliable results for source localization can be obtained in the presence of evident EEG effects. Due to different levels of a priori knowledge, different source localization methods have different applicable scenarios. These findings provide new evidence for the validity of source localization analysis and a new perspective for the evaluation of source localization methods on real EEG data.

Spontaneous Necker-cube reversals may not be that spontaneous.

During observation of the ambiguous Necker cube, our perception suddenly reverses between two about equally possible 3D interpretations. During passive observation, perceptual reversals seem to be sudden and spontaneous. A number of theoretical approaches postulate destabilization of neural representations as a pre-condition for reversals of ambiguous figures. In the current study, we focused on possible Electroencephalogram (EEG) correlates of perceptual destabilization, that may allow prediction of an upcoming perceptual reversal. We presented ambiguous Necker cube stimuli in an onset-paradigm and investigated the neural processes underlying endogenous reversals as compared to perceptual stability across two consecutive stimulus presentations. In a separate experimental condition, disambiguated cube variants were alternated randomly, to exogenously induce perceptual reversals. We compared the EEG immediately before and during endogenous Necker cube reversals with corresponding time windows during exogenously induced perceptual reversals of disambiguated cube variants. For the ambiguous Necker cube stimuli, we found the earliest differences in the EEG between reversal trials and stability trials already 1 s before a reversal occurred, at bilateral parietal electrodes. The traces remained similar until approximately 1100 ms before a perceived reversal, became maximally different at around 890 ms (p = 7.59 × 10-6, Cohen's d = 1.35) and remained different until shortly before offset of the stimulus preceding the reversal. No such patterns were found in the case of disambiguated cube variants. The identified EEG effects may reflect destabilized states of neural representations, related to destabilized perceptual states preceding a perceptual reversal. They further indicate that spontaneous Necker cube reversals are most probably not as spontaneous as generally thought. Rather, the destabilization may occur over a longer time scale, at least 1 s before a reversal event, despite the reversal event as such being perceived as spontaneous by the viewer.

Agronomic Practices Alter Regulated Effects of Easily Extractable Glomalin-Related Soil Protein on Fruit Quality and Soil Properties of Satsuma Mandarin

Easily extractable glomalin-related soil protein (EEG) released from arbuscular mycorrhizal fungi exhibits many roles in soil and plants, while it is not clear whether the biostimulator alone or in combination with agronomic practices can improve soil characteristics and fruit quality in citrus. The objective of this study was to analyze the effects of foliar sprays of EEG, singly or in combination with fruit bagging (FB), reflective film mulching (RF), and grass-proof cloth mulching (GPC) on root mycorrhizal colonization rate, fruit internal and external quality, and soil structure and fertility on an early-ripening Satsuma mandarin “Oita 4” (Citrus unshiu cv. Oita 4). Exogenous EEG application increased root mycorrhizal colonization, while agronomic practices dramatically inhibited root mycorrhizal colonization in EEG-treated trees. Foliar EEG application significantly improved the internal and external quality of fruits, but the combination of three agronomic practices with EEG did not further enlarge the improved effect on the external quality of fruit and even produced an inhibitory effect. Also, the addition of RF further amplified the improved effect of EEG on the contents of fruit vitamin C and soluble solids. EEG application also significantly elevated sucrose in the fruit pith and juice sac, fructose in the fruit peel, fruit pith, segment membrane, and juice sac, and glucose in the fruit pith, segment membrane, and juice sac. Additional RF treatment could increase sucrose in the fruit peel and juice sac as well as glucose in the fruit peel to varying degrees in EEG-treated trees. EEG application also significantly increased soil organic carbon, mean weight diameter, nitrate nitrogen, ammonium nitrogen, available phosphorus, and available potassium levels, with additional agronomic practices almost reducing the promoting effect of EEG on these soil variables. It has been summarized that a single EEG application had positive effects on fruit quality and soil fertility, while the additional agronomic practices resulted in little change or even suppressed the positive effects of EEG.

N,N-dimethyltryptamine affects electroencephalography response in a concentration-dependent manner-A pharmacokinetic/pharmacodynamic analysis.

N,N-dimethyltryptamine (DMT) is a psychedelic substance and is being used as a research tool in investigations of the neurobiology behind the human consciousness using different brain imaging techniques. The effects of psychedelics have commonly been studied using electroencephalography (EEG) and have been shown to produce suppression of alpha power and increase in signal diversity. However, the relationship between DMT exposure and its EEG effects has never been quantified. In this work, a population pharmacokinetic/pharmacodynamic analysis was performed investigating the relationship between DMT plasma concentrations and its EEG effects. Data were obtained from a clinical study where DMT was administered by intravenous bolus dose to 13 healthy subjects. The effects on alpha power, beta power, and Lempel-Ziv complexity were evaluated. DMT was shown to fully suppress alpha power. Beta power was only partially suppressed, whereas an increase in Lempel-Ziv complexity was observed. The relationship between plasma concentrations and effects were described using effect compartment models with sigmoidal maximum inhibitory response or maximum stimulatory response models. Values of the concentration needed to reach half of the maximum response (EC50,e ) were estimated at 71, 137, and 54 nM for alpha, beta, and Lempel-Ziv complexity, respectively. A large amount of between-subject variability was associated with both beta power and Lempel-Ziv complexity with coefficients of variability of 75% and 77% for the corresponding EC50,e values, respectively. Alpha power appeared to be the most robust response, with a between-subject variability in EC50,e of 29%. Having a deeper understanding of these processes might prove beneficial in choosing appropriate doses and response biomarkers in the future clinical development of DMT.

Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer’s disease using EEG technology

BackgroundElectroencephalogram (EEG) has emerged as a non-invasive tool to detect the aberrant neuronal activity related to different stages of Alzheimer’s disease (AD). However, the effectiveness of EEG in the precise diagnosis and assessment of AD and its preclinical stage, amnestic mild cognitive impairment (MCI), has yet to be fully elucidated. In this study, we aimed to identify key EEG biomarkers that are effective in distinguishing patients at the early stage of AD and monitoring the progression of AD.MethodsA total of 890 participants, including 189 patients with MCI, 330 patients with AD, 125 patients with other dementias (frontotemporal dementia, dementia with Lewy bodies, and vascular cognitive impairment), and 246 healthy controls (HC) were enrolled. Biomarkers were extracted from resting-state EEG recordings for a three-level classification of HC, MCI, and AD. The optimal EEG biomarkers were then identified based on the classification performance. Random forest regression was used to train a series of models by combining participants’ EEG biomarkers, demographic information (i.e., sex, age), CSF biomarkers, and APOE phenotype for assessing the disease progression and individual’s cognitive function.ResultsThe identified EEG biomarkers achieved over 70% accuracy in the three-level classification of HC, MCI, and AD. Among all six groups, the most prominent effects of AD-linked neurodegeneration on EEG metrics were localized at parieto-occipital regions. In the cross-validation predictive analyses, the optimal EEG features were more effective than the CSF + APOE biomarkers in predicting the age of onset and disease course, whereas the combination of EEG + CSF + APOE measures achieved the best performance for all targets of prediction.ConclusionsOur study indicates that EEG can be used as a useful screening tool for the diagnosis and disease progression evaluation of MCI and AD.