Sleep spindles have emerged as valuable biomarkers for assessing cognitive abilities and related disorders, underscoring the importance of their detection in clinical research. However, template matching-based algorithms using fixed templates may not be able to fully adapt to spindles of different durations. Moreover, inspired by the multiscale feature extraction of images, the use of multiscale feature extraction methods can be used to better adapt to spindles of different frequencies and durations. Therefore, this study proposes a novel automatic spindle detection algorithm based on elastic time windows and spatial pyramid pooling (SPP) for extracting multiscale features. The algorithm utilizes elastic time windows to segment electroencephalogram (EEG) signals, enabling the extraction of features across multiple scales. This approach accommodates significant variations in spindle duration and polarization positioning during different EEG epochs. Additionally, spatial pyramid pooling is integrated into a depthwise separable convolutional (DSC) network to perform multiscale pooling on the segmented spindle signal features at different scales. Compared with existing template matching algorithms, this algorithm's spindle wave polarization positioning is more consistent with the real situation. Experimental results conducted on the public dataset DREAMS show that the average accuracy of this algorithm reaches 95.75%, with an average negative predictive value (NPV) of 96.55%, indicating its advanced performance. The effectiveness of each module was verified through thorough ablation experiments. More importantly, the algorithm shows strong robustness when faced with changes in different experimental subjects. This feature makes the algorithm more accurate at identifying sleep spindles and is expected to help experts automatically detect spindles in sleep EEG signals, reduce the workload and time of manual detection, and improve efficiency.

Non-invasive brain mapping using navigated transcranial magnetic stimulation (nTMS) is a valuable tool prior to resection of malignant brain tumors. With nTMS motor mapping, it is additionally possible to analyze the function of the motor system and to evaluate tumor-induced neuroplasticity. Distinct changes in motor cortex excitability induced by certain malignant brain tumors are a focal point of research. A retrospective single-center study was conducted involving patients with malignant brain tumors. Clinical data, resting motor threshold (rMT), and nTMS-based tractography were evaluated. The interhemispheric rMT-ratio (rMTTumor/rMTControl) was calculated for each extremity and considered pathological if it was >110% or <90%. Distances between the corticospinal tract and the tumor (lesion-to-tract-distance - LTD) were measured. 49 patients were evaluated. 16 patients (32.7%) had a preoperative motor deficit. The cohort comprised 22 glioblastomas (44.9%), 5 gliomas of Classification of Tumors of the Central Nervous System (CNS WHO) grade 3 (10.2%), 6 gliomas of CNS WHO grade 2 (12.2%) and 16 cerebral metastases (32.7%). 26 (53.1%) had a pathological rMT-ratio for the upper extremity and 35 (71.4%) for the lower extremity. All patients with tumor-induced motor deficits had pathological interhemispheric rMT-ratios, and presence of tumor-induced motor deficits was associated with infiltration of the tumor to the nTMS-positive cortex (p = 0.04) and shorter LTDs (all p < 0.021). Pathological interhemispheric rMT-ratio for the upper extremity was associated with cerebral metastases, but not with gliomas (p = 0.002). Our study underlines the diagnostic potential of nTMS motor mapping to go beyond surgical risk stratification. Pathological alterations in motor cortex excitability can be measured with nTMS mapping. Pathological cortical excitability was more frequent in cerebral metastases than in gliomas.

Background: Neurofeedback is a non-invasive brain training technique used to enhance and treat hyperactivity disorder by altering the patterns of brain activity. Nonetheless, the extent of enhancement by neurofeedback varies among individuals/patients and many of them are irresponsive to this treatment technique. Therefore, several studies have been conducted to predict the effectiveness of neurofeedback training including the theta/beta protocol with a specific emphasize on slow cortical potential (SCP) before initiating treatment, as well as examining SCP criteria according to age and sex criteria in diverse populations. While some of these studies failed to make accurate predictions, others have demonstrated low success rates. This study explores functional connections within various brain lobes across different frequency bands of electroencephalogram (EEG) signals and the value of phase locking is used to predict the potential effectiveness of neurofeedback treatment before its initiation. Methods: This study utilized EEG data from the Mendelian database. In this database, EEG signals were recorded during neurofeedback sessions involving 60 hyperactive students aged 7–14 years, irrespective of sex. These students were categorized into treatable and non-treatable. The proposed method includes a five-step algorithm. Initially, the data underwent preprocessing to reduce noise using a multi-stage filtering process. The second step involved extracting alpha and beta frequency bands from the preprocessed EEG signals, with a particular emphasis on the EEG recorded from sessions 10 to 20 of neurofeedback therapy. In the third step, the method assessed the disparity in brain signals between the two groups by evaluating functional relationships in different brain lobes using the phase lock value, a crucial data characteristic. The fourth step focused on reducing the feature space and identifying the most effective and optimal electrodes for neurofeedback treatment. Two methods, the probability index (p-value) via a t-test and the genetic algorithm, were employed. These methods showed that the optimal electrodes were in the frontal lobe and central cerebral cortex, notably channels C3, FZ, F4, CZ, C4, and F3, as they exhibited significant differences between the two groups. Finally, in the fifth step, machine learning classifiers were applied, and the results were combined to generate treatable and non-treatable labels for each dataset. Results: Among the classifiers, the support vector machine and the boosting method demonstrated the highest accuracy when combined. Consequently, the proposed algorithm successfully predicted the treatability of individuals with hyperactivity in a short time and with limited data, achieving an accuracy of 90.6% in the neurofeedback method. Additionally, it effectively identified key electrodes in neurofeedback treatment, reducing their number from 32 to 6. Conclusions: This study introduces an algorithm with a 90.6% accuracy for predicting neurofeedback treatment outcomes in hyperactivity disorder, significantly enhancing treatment efficiency by identifying optimal electrodes and reducing their number from 32 to 6. The proposed method enables the prediction of patient responsiveness to neurofeedback therapy without the need for numerous sessions, thus conserving time and financial resources.

Methamphetamine (METH) is a highly addictive drug that directly affects the central nervous system. METH use not only harms the user's health but also poses risks and costs to society. Prolonged METH dependence has been shown to impair cognition, which may be the primary factor in impulsive drug-seeking behaviors and high relapse rates. However, the molecular mechanisms underlying METH addiction and METH-induced cognitive decline remain poorly understood. To illuminate the potential molecular mechanisms underpinning METH addiction, we compared serum protein expression levels between 12 long-term METH users and 12 healthy controls using label-free quantitative proteomics. Bioinformatic analyses were conducted to determine functional networks and protein-protein interactions. In total, 23 differentially expressed proteins were identified between the two groups. The differentially expressed proteins were related to cognitive dysfunction, neuroinflammation, immune impairment, metabolic disturbances, and calcium binding and regulation. These 23 proteins may underpin the multi-system damage induced by chronic METH exposure. Our findings provide novel insights into the molecular basis of METH addiction and inform potential prevention and treatment strategies for individuals with METH dependence.

Repetitive mild traumatic brain injury (rmTBI) often occurs in individuals engaged in contact sports, particularly boxing. This study aimed to elucidate the effects of rmTBI on phase-locking value (PLV)-based graph theory and functional network architecture in individuals with boxing-related injuries in five frequency bands by employing resting-state electroencephalography (EEG). Twenty-fore professional boxers and 25 matched healthy controls were recruited to perform a resting-state task, and their noninvasive scalp EEG data were collected simultaneously. Based on the construction of PLV matrices for boxers and controls, phase synchronization and graph-theoretic characteristics were identified in each frequency band. The significance of the calculated functional brain networks between the two populations was analyzed using a network-based statistical (NBS) approach. Compared to controls, boxers exhibited an increasing trend in PLV synchronization and notable differences in the distribution of functional centers, especially in the gamma frequency band. Additionally, attenuated nodal network parameters and decreased small-world measures were observed in the theta, beta, and gamma bands, suggesting that the functional network efficiency and small-world characteristics were significantly weakened in boxers. NBS analysis revealed that boxers exhibited a significant increase in network connectivity strength compared to controls in the theta, beta, and gamma frequency bands. The functional connectivity of the significance subnetworks exhibited an asymmetric distribution between the bilateral hemispheres, indicating that the optimized organization of information integration and segregation for the resting-state networks was imbalanced and disarranged for boxers. This is the first study to investigate the underlying deficits in PLV-based graph-theoretic characteristics and NBS-based functional networks in patients with rmTBI from the perspective of whole-brain resting-state EEG. Joint analyses of distinctive graph-theoretic representations and asymmetrically hyperconnected subnetworks in specific frequency bands may serve as an effective method to assess the underlying deficiencies in resting-state network processing in patients with sports-related rmTBI.

The feeling of emotional tension, restlessness, pressure, and inability to relax is referred to as psychological stress. Although it is unclear how psychological stress affects neurobiological processes, several factors are thought to be involved, including central and peripheral neuroinflammation, structural degeneration in the prefrontal cortex and hippocampus, alterations in fear neurocircuitry, and neuroplasticity. Aside from data relating cognitive impairment to chronic low-grade inflammatory stress, there is growing evidence linking mental stress, oxidative stress, and systemic inflammation to the development of psychological disorders. After chronic and acute illnesses, insomnia, depression, anxiety, posttraumatic stress disorder, and cognitive impairment were reported. Cognitive impairment is exacerbated by systemic and central inflammatory processes. There is uncertainty about the potential mechanisms causing these symptoms, although they are likely complex, with systemic inflammation playing a significant role. Therefore, this review aims to investigate the role of inflammation in stress-induced cognitive impairment. Depicting the inflammatory mechanisms of cognitive impairment is critical for understanding and treating illnesses, such as chronic stress exposure and anxiety disorders.

Magnetoencephalography (MEG) is a non-invasive imaging technique for directly measuring the external magnetic field generated from synchronously activated pyramidal neurons in the brain. The optically pumped magnetometer (OPM) is known for its less expensive, non-cryogenic, movable and user-friendly custom-design provides the potential for a change in functional neuroimaging based on MEG. An array of OPMs covering the opposite sides of a subject's head is placed inside a magnetically shielded room (MSR) and responses evoked from the auditory cortices are measured. High signal-to-noise ratio auditory evoked response fields (AEFs) were detected by a wearable OPM-MEG system in a MSR, for which a flexible helmet was specially designed to minimize the sensor-to-head distance, along with a set of bi-planar coils developed for background field and gradient nulling. Neuronal current sources activated in AEF experiments were localized and the auditory cortices showed the highest activities. Performance of the hybrid optically pumped magnetometer-magnetoencephalography/electroencephalography (OPM-MEG/EEG) system was also assessed. The multi-channel OPM-MEG system performs well in a custom built MSR equipped with bi-planar coils and detects human AEFs with a flexible helmet. Moreover, the similarities and differences of auditory evoked potentials (AEPs) and AEFs are discussed, while the operation of OPM-MEG sensors in conjunction with EEG electrodes provides an encouraging combination for the exploration of hybrid OPM-MEG/EEG systems.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a well-established treatment for the motor symptoms of Parkinson's disease (PD). While PD is primarily characterized by motor symptoms such as tremor, rigidity, and bradykinesia, it also involves a range of non-motor symptoms, and anxiety is one of the most common. The relationship between PD and anxiety is complex and can be a result of both pathological neural changes and the psychological and emotional impacts of living with a chronic progressive condition. Managing anxiety in PD is critical for improving the patients' quality of life. However, patients undergoing STN DBS can occasionally experience increased anxiety. This study investigates changes in risk-avoidant behavior following STN DBS in a pre-motor animal model of PD under chronic and acute unilateral high frequency stimulation. No significant changes in risk-avoidant behaviors were observed in rats who underwent STN DBS compared with sham stimulation controls. Chronic stimulation prevented sensitization in the elevated zero maze. These results suggest that unilateral stimulation of the STN may have minimal effects on risk-avoidant behaviors in PD. However, additional research is required to fully understand the mechanisms responsible for changes in anxiety during STN DBS for PD.

To enhance the information transfer rate (ITR) of a steady-state visual evoked potential (SSVEP)-based speller, more characters with flickering symbols should be used. Increasing the number of symbols might reduce the classification accuracy. A hybrid brain-computer interface (BCI) improves the overall performance of a BCI system by taking advantage of two or more control signals. In a simultaneous hybrid BCI, various modalities work with each other simultaneously, which enhances the ITR. In our proposed speller, simultaneous combination of electromyogram (EMG) and SSVEP was applied to increase the ITR. To achieve 36 characters, only nine stimulus symbols were used. Each symbol allowed the selection of four characters based on four states of muscle activity. The SSVEP detected which symbol the subject was focusing on and the EMG determined the target character out of the four characters dedicated to that symbol. The frequency rate for character encoding was applied in the EMG modality and latency was considered in the SSVEP modality. Online experiments were carried out on 10 healthy subjects. The average ITR of this hybrid system was 96.1 bit/min with an accuracy of 91.2%. The speller speed was 20.9 char/min. Different subjects had various latency values. We used an average latency of 0.2 s across all subjects. Evaluation of each modality showed that the SSVEP classification accuracy varied for different subjects, ranging from 80% to 100%, while the EMG classification accuracy was approximately 100% for all subjects. Our proposed hybrid BCI speller showed improved system speed compared with state-of-the-art systems based on SSVEP or SSVEP-EMG, and can provide a user-friendly, practical system for speller applications.

Pannexin1 (Panx1) is a membrane channel expressed in different cells of the nervous system and is involved in several pathological conditions, including pain and inflammation. At the central nervous system, the role of Panx1 is already well-established. However, in the periphery, there is a lack of information regarding the participation of Panx1 in neuronal sensitization. The dorsal root ganglion (DRG) is a critical structure for pain processing and modulation. For this reason, understanding the molecular mechanism in the DRG associated with neuronal hypersensitivity has become highly relevant to discovering new possibilities for pain treatment. Here, we aimed to investigate the role of Panx1 in acute nociception and peripheral inflammatory and neuropathic pain by using two different approaches. Rats were treated with a selective Panx1 blocker peptide (10Panx) into L5-DRG, followed by ipsilateral intraplantar injection of carrageenan, formalin, or capsaicin. DRG neuronal cells were pre-treated with 10Panx and stimulated by capsaicin to evaluate calcium influx. Panx1 knockout mice (Panx1-KO) received carrageenan or capsaicin into the paw and paclitaxel intraperitoneally. The von Frey test was performed to measure the mechanical threshold of rats' and mice's paws before and after each treatment. Pharmacological blockade of Panx1 in the DRG of rats resulted in a dose-dependent decrease of mechanical allodynia triggered by carrageenan, and nociception decreased in the second phase of formalin. Nociceptive behavior response induced by capsaicin was significantly lower in rats treated with Panx1 blockade into DRG. Neuronal cells with Panx1 blockage showed lower intracellular calcium response than untreated cells after capsaicin administration. Accordingly, Panx1-KO mice showed a robust reduction in mechanical allodynia after carrageenan and a lower nociceptive response to capsaicin. A single dose of paclitaxel promoted acute mechanical pain in wildtype (WT) but not in Panx1-KO mice. Four doses of chemotherapy promoted chronic mechanical allodynia in both genotypes, although Panx1-KO mice had significant ablation in the first eight days. Our findings suggest that Panx1 is critical for developing peripheral inflammatory pain and acute nociception involving transient receptor potential vanilloid subtype 1 (TRPV1) but is not essential for neuropathic pain chronicity.