Effects of 15-Day -6° Head-Down Bed Rest and HD-tDCS on Cognitive Functions.

A favorable cognitive state, efficient attention, and sharp alertness are essential for successful space missions. To explore the effects of microgravity on working memory, attention, and alertness and to find effective approaches to improve them, we recruited 17 healthy participants (control: 9, active: 8) to be involved in a 15-day -6° head-down bed rest (HDBR). A 30 min HD-tDCS was applied to the active group every other day during HDBR.

Improving EEG-Based Cross-Subject Mental Workload Classification Performance with Euclidean-Aligned Periodic and Aperiodic Features.

Enhancing the cross-subject classification performance of EEG-based mental workload (MWL) monitoring models poses a significant challenge. Traditional methods require gathering calibration data for new users to prevent performance decline. However, the calibration data collection process is time-consuming and labor-intensive.

Transcutaneous auricular vagus nerve stimulation does not modulate working memory capacity but alters pupillary responses in the change detection task.

Transcutaneous vagus nerve stimulation (taVNS) offers an effective, non-invasive alternative to implantable vagus nerve stimulation and it is considered to be a promising cognitive modulation tool. However, at present, the effect of taVNS on working memory is not clear, Simultaneously, the potential pupillary responses stemming from taVNS require more empirical inquiry. Herein, we investigated the influence of taVNS on working memory capacity and its link to pupillary responses during the change detection task.

Disrupted small-world architecture and altered default mode network topology of brain functional network in college students with subclinical depression.

Background: Subclinical depression (ScD), serving as a significant precursor to depression, is a prevalent condition in college students and imposes a substantial health service burden. However, the brain network topology of ScD remains poorly understood, impeding our comprehension of the neuropathology underlying ScD.

Methods: Functional networks of individuals with ScD (n = 26) and healthy controls (HCs) (n = 33) were constructed based on functional magnetic resonance imaging data.

High-Frequency SSVEP-BCI With Row-Column Dual-Frequency Encoding and Decoding Strategy for Reduced Training Data.

Steady-state visual evoked potentials (SSVEP)-based brain-computer interfaces (BCIs) have the potential to be utilized in various fields due to their high accuracies and information transfer rates (ITR). High-frequency (HF) visual stimuli have shown promise in reducing visual fatigue and enhancing user comfort. However, these HF-SSVEP-BCIs often face limitations in the number of commands and typically require extensive individual training data to achieve high performance.

Improving the Performance of Individually Calibrated SSVEP Classification by Rhythmic Entrainment Source Separation.

Objective: The supervised decoding algorithms of Steady-State Visual Evoked Potentials (SSVEP) have achieved remarkable performance with sufficient training data. However, these methods have typically failed to achieve acceptable performance in single-trial training scenarios.

Methods: To address this challenge, we propose a method to enhance SSVEP classification performance using less training data by employing Rhythmic Entrainment Source Separation (RESS) to construct spatial filters.

Tonic and Event-Related Phasic Transcutaneous Auricular Vagus Nerve Stimulation Alters Pupil Responses in the Change-Detection Task.

Background: Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a potential modulator of cognitive behavior that activates the locus coeruleus-noradrenaline (LC-NA) system. Previous studies explored both phasic and tonic taVNS by investigating their impact on LC-NA markers such as pupil dilation and heart rate variability (HRV).

Objective: Inconsistencies persist in the identification of reliable markers for assessing the effects of taVNS on noradrenergic activity.

Correction to "Enhancing Detection of Control State for High-Speed Asynchronous SSVEP-BCIs Using Frequency-Specific Framework".

IN the above article [1], we found the formula (1) is presented incorrectly because of an error in the formula editing process. The correction is as follows: ITR=[logK+PlogP+(1-p)log(1-P/K-1)]×60/T (1).

Enhancing SSVEP-BCI Performance Under Fatigue State Using Dynamic Stopping Strategy.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have emerged as a prominent technology due to their high information transfer rate, rapid calibration time, and robust signal-to-noise ratio. However, a critical challenge for practical applications is performance degradation caused by user fatigue during prolonged use. This work proposes novel methods to address this challenge by dynamically adjusting data acquisition length and updating detection models based on a fatigue-aware stopping strategy.

Evidence for modulation of EEG microstates by mental workload levels and task types.

Electroencephalography (EEG) microstate analysis has become a popular tool for studying the spatial and temporal dynamics of large-scale electrophysiological activities in the brain in recent years. Four canonical topographies of the electric field (classes A, B, C, and D) have been widely identified, and changes in microstate parameters are associated with several psychiatric disorders and cognitive functions. Recent studies have reported the modulation of EEG microstate by mental workload (MWL).

Enhancing EEG-based cross-day mental workload classification using periodic component of power spectrum.

. The day-to-day variability of electroencephalogram (EEG) poses a significant challenge to decode human brain activity in EEG-based passive brain-computer interfaces (pBCIs). Conventionally, a time-consuming calibration process is required to collect data from users on a new day to ensure the performance of the machine learning-based decoding model, which hinders the application of pBCIs to monitor mental workload (MWL) states in real-world settings.

Enhancing SSVEP Identification With Less Individual Calibration Data Using Periodically Repeated Component Analysis.

Objective: Spatial filtering and template matching-based steady-state visually evoked potentials (SSVEP) identification methods usually underperform in SSVEP identification with small-sample-size calibration data, especially when a single trial of data is available for each stimulation frequency.

Methods: In contrast to the state-of-the-art task-related component analysis (TRCA)-based methods, which construct spatial filters and SSVEP templates based on the inter-trial task-related components in SSVEP, this study proposes a method called periodically repeated component analysis (PRCA), which constructs spatial filters to maximize the reproducibility across periods and constructs synthetic SSVEP templates by replicating the periodically repeated components (PRCs). We also introduced PRCs into two improved variants of TRCA.

Lasting enhancements in neural efficiency by multi-session transcranial direct current stimulation during working memory training.

The neural basis for long-term behavioral improvements resulting from multi-session transcranial direct current stimulation (tDCS) combined with working memory training (WMT) remains unclear. In this study, we used task-related electroencephalography (EEG) measures to investigate the lasting neurophysiological effects of anodal high-definition (HD)-tDCS applied over the left dorsolateral prefrontal cortex (dlPFC) during a challenging WMT. Thirty-four healthy young adults were randomized to sham or active tDCS groups and underwent ten 30-minute training sessions over ten consecutive days, preceded by a pre-test and followed by post-tests performed one day and three weeks after the last session, respectively, by performing high-load WM tasks along with EEG recording.

[Effects of transcranial direct current stimulation on event-related potentials of mental rotation].

There are few researches on the modulation effect of transcranial direct current stimulation(tDCS) on complex spatial cognition. Especially, the influence of tDCS on the neural electrophysiological response in spatial cognition is not yet clear. This study selected the classic spatial cognition task paradigm (three-dimensional mental rotation task) as the research object.

Neurophysiological mechanisms of transcranial alternating current stimulation.

Neuronal oscillations are the primary basis for precise temporal coordination of neuronal processing and are linked to different brain functions. Transcranial alternating current stimulation (tACS) has demonstrated promising potential in improving cognition by entraining neural oscillations. Despite positive findings in recent decades, the results obtained are sometimes rife with variance and replicability problems, and the findings translation to humans is quite challenging.

Enhancing Detection of Control State for High-Speed Asynchronous SSVEP-BCIs Using Frequency-Specific Framework.

This study proposed a novel frequency-specific (FS) algorithm framework for enhancing control state detection using short data length toward high-performance asynchronous steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI). The FS framework sequentially incorporated task-related component analysis (TRCA)-based SSVEP identification and a classifier bank containing multiple FS control state detection classifiers. For an input EEG epoch, the FS framework first identified its potential SSVEP frequency using the TRCA-based method and then recognized its control state using one of the classifiers trained on the features specifically related to the identified frequency.

Neurophysiological markers of depression detection and severity prediction in first-episode major depressive disorder.

Objective: Deviant γ auditory steady-state responses (γ-ASSRs) have been documented in some psychiatric disorders. Nevertheless, the role of γ-ASSR in drug-naïve first-episode major depressive disorder (FEMD) patients remains equivocal. This study aimed to examine whether γ-ASSRs are impaired in FEMD patients and predict depression severity.

Altered fronto-central theta-gamma coupling in major depressive disorder during auditory steady-state responses.

Objective: Neural oscillations during sensory and cognitive events interact at different frequencies. However, such evidence in major depressive disorder (MDD) remains scarce. We explored the possible abnormal neural oscillations in MDD by analyzing theta-phase/gamma-amplitude coupling (TGC).

Depression recognition using a proposed speech chain model fusing speech production and perception features.

Background: Increasing depression patients puts great pressure on clinical diagnosis. Audio-based diagnosis is a helpful auxiliary tool for early mass screening. However, current methods consider only speech perception features, ignoring patients' vocal tract changes, which may partly result in the poor recognition.

Common and unique neural activities in subclinical depression and major depressive disorder indicate the development of brain impairments in different depressive stages.

Background: Subclinical depression (SD) and major depressive disorder (MDD) can be considered as the early and late stages of depression, but the characteristics of intrinsic neural activity in different depressive stages are largely unknown.

Methods: Twenty-six SD, 36 MDD subjects and 33 well-matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Voxel-wise regional homogeneity (ReHo) was analyzed to explore the alterations of intrinsic neural activity, and machine learning classification based on ReHo features was performed to assess potential performance for diagnostic classification.

Altered gamma oscillations and beta-gamma coupling in drug-naive first-episode major depressive disorder: Association with sleep and cognitive disturbance.

Objective: Gamma oscillations contribute to the pathogenesis mechanisms of major depressive disorder (MDD) have been proposed, but gamma activity is not well characterized. This study is the first attempt to investigate the altered gamma oscillations in first-episode MDD, particularly the beta-gamma coupling, and to determine the potential symptomatic relationship with the identified gamma dysregulation.

Methods: Resting electroencephalography was recorded for 43 drug-naive first-episode MDD and 57 healthy control (HC) subjects.

Transcranial Direct Current Stimulation Over the Left Dorsolateral Prefrontal Cortex Reduced Attention Bias Toward Negative Facial Expression: A Pilot Study in Healthy Subjects.

Research in the cognitive neuroscience field has shown that individuals with a stronger attention bias for negative information had higher depression risk, which may be the underlying pathogenesis of depression. This dysfunction of affect-biased attention also represents a decline in emotion regulation ability. Clinical studies have suggested that transcranial direct current stimulation (tDCS) treatment can improve the symptoms of depression, yet the neural mechanism behind this improvement is still veiled.

Cross-Task Consistency of Electroencephalography-Based Mental Workload Indicators: Comparisons Between Power Spectral Density and Task-Irrelevant Auditory Event-Related Potentials.

Mental workload (MWL) estimators based on ongoing electroencephalography (EEG) and event-related potentials (ERPs) have shown great potentials to build adaptive aiding systems for human-machine systems by estimating MWL in real time. However, extracting EEG features which are consistent in indicating MWL across different tasks is still one of the critical challenges. This study attempts to compare the cross-task consistency in indexing MWL variations between two commonly used EEG-based MWL indicators, power spectral density (PSD) of ongoing EEG and task-irrelevant auditory ERPs (tir-aERPs).

Discriminating subclinical depression from major depression using multi-scale brain functional features: A radiomics analysis.

Background: The diagnosis of subclinical depression (SD) currently relies exclusively on subjective clinical scores and structured interviews, which shares great similarities with major depression (MD) and increases the risk of misdiagnosis of SD and MD. This study aimed to develop a method of disease classification for SD and MD by resting-state functional features using radiomics strategy.

Methods: Twenty-six SD, 36 MD subjects and 33 well-matched healthy controls (HC) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI).