Shooting is a fine sport that is greatly influenced by mental state, and the neural activity of brain in the preparation stage of shooting has a direct influence on the level of shooting. In order to explore the brain neural mechanism in the preparation stage of pistol shooting under audiovisual restricted conditions, and to reveal the intrinsic relationship between brain activity and shooting behavior indicators, the electroencephalography (EEG) signals and seven shooting behaviors including shooting performance, gun holding stability, and firing stability, were experimentally captured from 30 shooters, these shooters performed pistol shooting under three conditions, normal, dim, and noisy. Using EEG microstates combined with standardized low-resolution brain electromagnetic tomography (sLORETA) traceability analysis method, we investigated the difference between the microstates characteristics under audiovisual restricted conditions and normal condition, the relationship between the microstates characteristics and the behavioral indicators during the shooting preparation stage under different conditions. The experimental results showed that microstate 1 corresponded to microstate A, microstate 2 corresponded to microstate B, and microstate 4 corresponded to microstate D; Microstate 3 was a unique template, which was localized in the occipital lobe, its function was to generate the \"vision for action\"; The dim condition significantly reduced the shooter\'s performance, whereas the noisy condition had less effect on the shooter\'s performance; In audiovisual restricted conditions, the microstate characteristics were significantly different from those in the normal condition. Microstate 4\' parameters decreased significantly while microstate 3\' parameters increased significantly under restricted visual and auditory conditions; Dim condition required more shooting skills from the shooter; There was a significant relationship between characteristics of microstates and indicators of shooting behavior; It was concluded that in order to obtain good shooting performance, shooters should improve attention and concentrate on the adjustment of collimator and target\'s center leveling relation, but the focus was slightly different in the three conditions; Microstates that are more important for accomplishing the task have less variation in their characteristics over time; Similar conclusions to previous studies were obtained at the same time, i.e., increased visual attention prior to shooting is detrimental to shooting performance, and there is a high positive correlation with microstate D for task completion. The experimental results further reveal the brain neural mechanism in the shooting preparation stage, and the extracted neural markers can be used as effective functional indicators for monitoring the brain state in the shooting preparation stage of pistols.

BACKGROUND: Transcranial direct current stimulation (tDCS) is a safe, accessible, and promising therapeutic approach for obsessive-compulsive disorder (OCD).
OBJECTIVE: This study aimed to evaluate the effect of tDCS on electroencephalography (EEG) microstates and identify potential biomarkers to predict efficacy.
METHODS: A total of 24 individuals diagnosed with OCD underwent ten sessions of tDCS targeting the orbitofrontal cortex, while 27 healthy individuals were included as controls. Microstates A, B, C, and D were extracted before and after tDCS. A comparative analysis of microstate metrics was performed between the OCD and the healthy control groups, as well as within the OCD group before and after tDCS. Multiple linear regression analysis was performed to identify potential biomarkers of tDCS.
RESULTS: Comparison to healthy controls, the OCD group exhibited a significantly reduced duration of microstate A and increased occurrence of microstate D. The transition between microstates A and C was significantly different between patients with OCD and healthy controls and was no longer observed following tDCS. Multiple linear regression analysis revealed that the duration of microstate C was associated with an improvement OCD symptom after tDCS.
CONCLUSIONS: The results revealed an aberrant large-scale EEG brain network that could be modulated by tDCS. In particular, the duration of EEG microstate C may be a neurophysiological characteristic associated with the therapeutic effects of tDCS on OCD.

BACKGROUND: Gender carries important information related to male and female characteristics, and a large number of studies have attempted to use physiological measurement methods for gender classification. Although previous studies have shown that there exist statistical differences in some Electroencephalographic (EEG) microstate parameters between males and females, it is still unknown that whether these microstate parameters can be used as potential biomarkers for gender classification based on machine learning.
METHODS: We used two independent resting-state EEG datasets: the first dataset included 74 females and matched 74 males, and the second one included 42 males and matched 42 females. EEG microstate analysis based on modified k-means clustering method was applied, and temporal parameter and nonlinear characteristics (sample entropy and Lempel-Ziv complexity) of EEG microstate sequences were extracted to compare between males and females. More importantly, these microstate temporal parameters and complexity were tried to train six machine learning methods for gender classification.
RESULTS: We obtained five common microstates for each dataset and each group. Compared with the male group, the female group has significantly higher temporal parameters of microstate B, C, E and lower temporal parameters of microstate A and D, and higher complexity of microstate sequence. When using combination of microstate temporal parameters and complexity or only microstate temporal parameters as classification features in an independent test set (the second dataset), we achieved 95.2% classification accuracy.
CONCLUSIONS: Our research findings indicate that the dynamics of microstate have considerable Gender-specific alteration. EEG microstates can be used as neurophysiological biomarkers for gender classification.

The alteration of neural interactions across different cerebral perfusion states remains unclear. This study aimed to fulfill this gap by examining the longitudinal brain dynamic information interactions before and after cerebral reperfusion. Electroencephalogram in eyes-closed state at baseline and postoperative 7-d and 3-month follow-ups (moyamoya disease: 20, health controls: 23) were recorded. Dynamic network analyses were focused on the features and networks of electroencephalogram microstates across different microstates and perfusion states. Considering the microstate features, the parameters were disturbed of microstate B, C, and D but preserved of microstate A. The transition probabilities of microstates A-B and B-D were increased to play a complementary role across different perfusion states. Moreover, the microstate variability was decreased, but was significantly improved after cerebral reperfusion. Regarding microstate networks, the functional connectivity strengths were declined, mainly within frontal, parietal, and occipital lobes and between parietal and occipital lobes in different perfusion states, but were ameliorated after cerebral reperfusion. This study elucidates how dynamic interaction patterns of brain neurons change after cerebral reperfusion, which allows for the observation of brain network transitions across various perfusion states in a live clinical setting through direct intervention.

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Multitasking with two or more media and devices has become increasingly common in our daily lives. The impact of chronic media multitasking on our cognitive abilities has received extensive concern. Converging studies have shown that heavy media multitaskers (HMM) have a greater demand for sensation seeking and are more easily distracted by task-irrelevant information than light media multitaskers (LMM). In this study, we analyzed the electroencephalogram data recorded during resting-state periods to investigate whether HMM and LMM differ with regard to basic resting network activation. Microstate analysis revealed that the activation of the attention network is weakened while the activation of the salience network is enhanced in HMM compared to LMM. This suggests that HMM\'s attention control is more likely to be guided by surrounding stimuli, which indirectly supports the deficit-producing hypothesis. Moreover, our results revealed that HMM had an enhanced visual network and may feel less comfortable than LMM during resting-state periods with eyes closed, supporting the view that HMM require more sensation seeking than LMM. Taken together, these results indicate that chronic media multitasking leads to HMM allocating attention in a bottom-up or stimulus-driven manner, while LMM deploy a top-down approach.

UNASSIGNED: The quantification of electroencephalography (EEG) microstates is an effective method for analyzing synchronous neural firing and assessing the temporal dynamics of the resting state of the human brain. Transcranial photobiomodulation (tPBM) is a safe and effective modality to improve human cognition. However, it is unclear how prefrontal tPBM neuromodulates EEG microstates both temporally and spectrally.
UNASSIGNED: 64-channel EEG was recorded from 45 healthy subjects in both 8-min active and sham tPBM sessions, using a 1064-nm laser applied to the right forehead of the subjects. After EEG data preprocessing, time-domain EEG microstate analysis was performed to obtain four microstate classes for both tPBM and sham sessions throughout the pre-, during-, and post-stimulation periods, followed by extraction of the respective microstate parameters. Moreover, frequency-domain analysis was performed by combining multivariate empirical mode decomposition with the Hilbert-Huang transform.
UNASSIGNED: Statistical analyses revealed that tPBM resulted in (1) a significant increase in the occurrence of microstates A and D and a significant decrease in the contribution of microstate C, (2) a substantial increase in the transition probabilities between microstates A and D, and (3) a substantial increase in the alpha power of microstate D.
UNASSIGNED: These findings confirm the neurophysiological effects of tPBM on EEG microstates of the resting brain, particularly in class D, which represents brain activation across the frontal and parietal regions. This study helps to better understand tPBM-induced dynamic alterations in EEG microstates that may be linked to the tPBM mechanism of action for the enhancement of human cognition.

Negative bias in prospection may play a crucial role in driving and maintaining depression. Recent research suggests abnormal activation and functional connectivity in regions of the default mode network (DMN) during future event generation in depressed individuals. However, the neural dynamics during prospection in these individuals remain unknown. To capture network dynamics at high temporal resolution, we employed electroencephalogram (EEG) microstate analysis. We examined microstate properties during both positive and negative prospection in 35 individuals with subthreshold depression (SD) and 35 controls. We identified similar sets of four canonical microstates (A-D) across groups and conditions. Source analysis indicated that each microstate map partially overlapped with a subsystem of the DMN (A: verbal; B: visual-spatial; C: self-referential; and D: modulation). Notably, alterations in EEG microstates were primarily observed in negative prospection of individuals with SD. Specifically, when generating negative future events, the coverage, occurrence, and duration of microstate A increased, while the coverage and duration of microstates B and D decreased in the SD group compared to controls. Furthermore, we observed altered transitions, particularly involving microstate C, during negative prospection in the SD group. These altered dynamics suggest dysconnectivity between subsystems of the DMN during negative prospection in individuals with SD. In conclusion, we provide novel insights into the neural mechanisms of negative bias in depression. These alterations could serve as specific markers for depression and potential targets for future interventions.

UNASSIGNED: Non-invasive brain stimulation (NIBS) techniques are now widely used in patients with disorders of consciousness (DOC) for accelerating their recovery of consciousness, especially minimally conscious state (MCS). However, the effectiveness of single NIBS techniques for consciousness rehabilitation needs further improvement. In this regard, we propose to enhance from bottom to top the thalamic-cortical connection by using transcutaneous auricular vagus nerve stimulation (taVNS) and increase from top to bottom cortical-cortical connections using simultaneous high-definition transcranial direct current stimulation (HD-tDCS) to reproduce the network of consciousness.
UNASSIGNED: The study will investigate the effect and safety of simultaneous joint stimulation (SJS) of taVNS and HD-tDCS for the recovery of consciousness. We will enroll 84 MCS patients and randomize them into two groups: a single stimulation group (taVNS and HD-tDCS) and a combined stimulation group (SJS and sham stimulation). All patients will undergo a 4-week treatment. The primary outcome will be assessed using the coma recovery scale-revised (CRS-R) at four time points to quantify the effect of treatment: before treatment (T0), after 1 week of treatment (T1), after 2 weeks of treatment (T2), and after 4 weeks of treatment (T3). At the same time, nociception coma scale-revised (NCS-R) and adverse effects (AEs) will be collected to verify the safety of the treatment. The secondary outcome will involve an analysis of electroencephalogram (EEG) microstates to assess the response mechanisms of dynamic brain networks to SJS. Additionally, CRS-R and AEs will continue to be obtained for a 3-month follow-up (T4) after the end of the treatment.
UNASSIGNED: This study protocol aims to innovatively develop a full-time and multi-brain region combined neuromodulation paradigm based on the mesocircuit model to steadily promote consciousness recovery by restoring thalamocortical and cortical-cortical interconnections.

Microstates are analogous to characters in a language, and short fragments consisting of several microstates (k-mers) are analogous to words. We aimed to investigate whether microstate k-mers could be used as neurophysiological biomarkers to differentiate between depressed patients and normal controls. We utilized a bag-of-words model to process microstate sequences, using k-mers with a k range of 1-10 as terms, and the term frequency (TF) with or without inverse-document-frequency (IDF) as features. We performed nested cross-validation on Dataset 1 (27 patients and 26 controls) and Dataset 2 (34 patients and 30 controls) separately and then trained on one dataset and tested on the other. The best area under the curve (AUC) of 81.5% was achieved for the model with L1 regularization using the TF of 4-mers as features in Dataset 1, and the best AUC of 88.9% was achieved for the model with L1 regularization using the TF of 9-mers as features in Dataset 2. When Dataset 1 was used as the training set, the best AUC of predicting Dataset 2 was 74.1% for the model with L2 regularization using the TF-IDF of 9-mers as features, while the best AUC of predicting Dataset 1 was 70.2% for the model with L1 regularization using the TF of 8-mers as features. Our study provided novel insights into the potential of microstate k-mers as neurophysiological biomarkers for individual-level classification of depression. These may facilitate further exploration of microstate sequences using natural language processing techniques.