Unveiling Frequency-Specific Microstate Correlates of Anxiety and Depression Symptoms.

Electroencephalography (EEG) microstates are canonical voltage topographies that reflect the temporal dynamics of brain networks on a millisecond time scale. Abnormalities in broadband microstate parameters have been observed in subjects with psychiatric symptoms, indicating their potential as clinical biomarkers. Considering distinct information provided by specific frequency bands of EEG, we hypothesized that microstates in decomposed frequency bands could provide a more detailed depiction of the underlying neuropathological mechanism.

Contrastive learning of shared spatiotemporal EEG representations across individuals for naturalistic neuroscience.

Neural representations induced by naturalistic stimuli offer insights into how humans respond to stimuli in daily life. Understanding neural mechanisms underlying naturalistic stimuli processing hinges on the precise identification and extraction of the shared neural patterns that are consistently present across individuals. Targeting the Electroencephalogram (EEG) technique, known for its rich spatial and temporal information, this study presents a framework for Contrastive Learning of Shared SpatioTemporal EEG Representations across individuals (CL-SSTER).

Electrophysiological representations of multivariate human emotion experience.

Despite the fact that human daily emotions are co-occurring by nature, most neuroscience studies have primarily adopted a univariate approach to identify the neural representation of emotion (emotion experience within a single emotion category) without adequate consideration of the co-occurrence of different emotions (emotion experience across different emotion categories simultaneously). To investigate the neural representations of multivariate emotion experience, this study employed the inter-situation representational similarity analysis (RSA) method. Researchers used an EEG dataset of 78 participants who watched 28 video clips and rated their experience on eight emotion categories.

A Large Finer-grained Affective Computing EEG Dataset.

Affective computing based on electroencephalogram (EEG) has gained increasing attention for its objectivity in measuring emotional states. While positive emotions play a crucial role in various real-world applications, such as human-computer interactions, the state-of-the-art EEG datasets have primarily focused on negative emotions, with less consideration given to positive emotions. Meanwhile, these datasets usually have a relatively small sample size, limiting exploration of the important issue of cross-subject affective computing.

The EEG microstate representation of discrete emotions.

Understanding how human emotions are represented in our brain is a central question in the field of affective neuroscience. While previous studies have mainly adopted a modular and static perspective on the neural representation of emotions, emerging research suggests that emotions may rely on a distributed and dynamic representation. The present study aimed to explore the EEG microstate representations for nine discrete emotions (Anger, Disgust, Fear, Sadness, Neutral, Amusement, Inspiration, Joy and Tenderness).

Short-term PM exposure and cognitive function: Association and neurophysiological mechanisms.

Background: Although converging evidence has demonstrated that exposure to fine particulate matter (PM) caused adverse effects on brain structure and cognitive function, the association between the short-term exposure to PM and cognition dysfunction remained underexplored, especially possible neurophysiological mechanisms.

Methods: We conducted a longitudinal observational study with four repeated measurement sessions among 90 young adults from September 2020 to June 2021. During each measurement session, we measured participants' personal-level air pollution exposure for one week with portable monitors, followed by executive function assessment and electrophysiological signal recording at an assessment center.

Exploring EEG microstates for affective computing: decoding valence and arousal experiences during video watching.

Investigating the electroencephalography (EEG) correlates of human emotional experiences has attracted increasing interest in the field of affective computing. Substantial progress has been made during the past decades, mainly by using EEG features extracted from localized brain activities. The present study explored a brain network-based feature defined by EEG microstates for a possible representation of emotional experiences.

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges.

As the research on artificial intelligence booms, there is broad interest in brain-inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re-visited, better understood, and connected to electronics.

Variation in longitudinal trajectories of cortical sulci in normal elderly.

Sulcal morphology has been reported to change with age-related neurological diseases, but the trajectories of sulcal change in normal ageing in the elderly is still unclear. We conducted a study of sulcal morphological changes over seven years in 132 normal elderly participants aged 70-90 years at baseline, and who remained cognitively normal for the next seven years. We examined the fold opening and sulcal depth of sixteen (eight on each hemisphere) prominent sulci based on T1-weighted MRI using automated methods with visual quality control.