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Type 2 diabetes mellitus (T2DM) increases the risk of neurological diseases, yet how brain oscillations change as age and T2DM interact is not well characterized. To delineate the age and diabetic effect on neurophysiology, we recorded local field potentials with multichannel electrodes spanning the somatosensory cortex and hippocampus (HPC) under urethane anesthesia in diabetic and normoglycemic control mice, at 200 and 400 days of age. We analyzed the signal power of brain oscillations, brain state, sharp wave associate ripples (SPW-Rs), and functional connectivity between the cortex and HPC. We found that while both age and T2DM were correlated with a breakdown in long-range functional connectivity and reduced neurogenesis in the dentate gyrus and subventricular zone, T2DM further slowed brain oscillations and reduced theta-gamma coupling. Age and T2DM also prolonged the duration of SPW-Rs and increased gamma power during SPW-R phase. Our results have identified potential electrophysiological substrates of hippocampal changes associated with T2DM and age. The perturbed brain oscillation features and diminished neurogenesis may underlie T2DM-accelerated cognitive impairment.

Tau rhythms are largely defined by sound responsive alpha band (~8-13 Hz) oscillations generated largely within auditory areas of the superior temporal gyri. Studies of tau have mostly employed magnetoencephalography or intracranial recording because of taus elusiveness in the electroencephalogram. Here, we demonstrate that independent component analysis (ICA) decomposition can be an effective way to identify tau sources and study tau source activities in EEG recordings. Subjects (N = 18) were passively exposed to complex acoustic stimuli while the EEG was recorded from 68 electrodes across the scalp. Subjects data were split into 60 parallel processing pipelines entailing use of five levels of high-pass filtering (passbands of 0.1, 0.5, 1, 2, and 4 Hz), three levels of low-pass filtering (25, 50, and 100 Hz), and four different ICA algorithms (fastICA, infomax, adaptive mixture ICA [AMICA], and multi-model AMICA [mAMICA]). Tau-related independent component (IC) processes were identified from this data as being localized near the superior temporal gyri with a spectral peak in the 8-13 Hz alpha band. These tau ICs showed alpha suppression during sound presentations that was not seen for other commonly observed IC clusters with spectral peaks in the alpha range (e.g., those associated with somatomotor mu, and parietal or occipital alpha). The choice of analysis parameters impacted the likelihood of obtaining tau ICs from an ICA decomposition. Lower cutoff frequencies for high-pass filtering resulted in significantly fewer subjects showing a tau IC than more aggressive high-pass filtering. Decomposition using the fastICA algorithm performed the poorest in this regard, while mAMICA performed best. The best combination of filters and ICA model choice was able to identify at least one tau IC in the data of ~94% of the sample. Altogether, the data reveal close similarities between tau EEG IC dynamics and tau dynamics observed in MEG and intracranial data. Use of relatively aggressive high-pass filters and mAMICA decomposition should allow researchers to identify and characterize tau rhythms in a majority of their subjects. We believe adopting the ICA decomposition approach to EEG analysis can increase the rate and range of discoveries related to auditory responsive tau rhythms.

Background: Athletic performance can be measured with a variety of clinical and functional assessment techniques. There is a need to better understand the relationship between the brain's electrical activity and the body's physiological performance capabilities in real-time while performing physical tasks related to sport. Orthopedic functional assessments used to monitor the neuroplastic properties of the central nervous system lack objectivity and/or pertinent functionality specific to sport. The ability to assess brain wave activity with physiological metrics during functional exercises associated with sport has proven to be difficult and impractical in real-time sport settings. Quantitative electroencephalography or qEEG brain mapping is a unique, real-time comprehensive assessment of brain electrical activity performed in combination with physiometrics which offers insight to neurophysiological brain-to-body function. Brain neuroplasticity has been associated with differences in musculoskeletal performance among athletes, however comparative real-time normal data to benchmark performance capabilities is limited. Purpose/Design: This prospective, descriptive case series evaluated performance of task-driven activities using an innovative neurophysiological assessment technique of qEEG monitored neurophysiological responses to establish a comparative benchmark of performance capabilities in healthy, uninjured Division-I athletes. Methods: Twenty-eight healthy uninjured females (n=11) and males (n=17) NCAA Division-I athletes participated in real-time neurophysiological assessment using a Bluetooth, wireless 21-channel dry EEG headset while performing functional activities. Results: Uninjured athletes experienced standard and regulated fluctuations of brain wave activity in key performance indicators of attention, workload capacity and sensorimotor rhythm (SMR) asymmetries. Conclusion: qEEG neurophysiological real-time assessment concurrent with functional activities in uninjured, Division-I athletes may provide a performance capability benchmark. Real-time neurophysiological data can be used to monitor athletes' preparedness to participate in sport, rehabilitation progressions, assist in development of injury prevention programs, and return to play decisions. While this paper focuses on healthy, uninjured participants, results underscore the need to discen pre-injury benchmarks. Level of Evidence: 4 [ABSTRACT FROM AUTHOR]

The electroencephalogram (EEG) is a tool utilized to capture the intricate electrical dynamics within the brain, offering invaluable insights into neural activity. This method is pivotal in identifying potential disruptions in brain cell communication, aiding in the diagnosis of various neurological conditions such as epilepsy and sleep disorders. The examination of EEG waveform morphology and associated characteristics serves as a cornerstone in this diagnostic process. Of particular significance within EEG analysis are sleep spindles, intricate patterns of brain waves implicated in crucial cognitive functions including brain plasticity, learning, memory consolidation, and motor skills. Traditionally, the task of analyzing EEG data has rested upon neurologists, neurosurgeons, or trained medical technicians, a laborious and error-prone endeavor. This study endeavors to revolutionize EEG analysis by leveraging artificial intelligence (AI) methodologies, specifically deep learning object detection techniques, to visually identify and locate sleep spindles within EEG waveform images. The You Only Look Once (YOLOv4) methodology is employed for this purpose. A diverse array of convolutional neural network architectures is meticulously customized, trained, and evaluated to facilitate feature extraction for the YOLOv4 detector. Furthermore, novel YOLOX detection models are introduced and extensively compared against YOLOv4-based counterparts. The results reveal outstanding performance across various metrics, with both YOLOX and YOLOv4 demonstrating exceptional average precision (AP) scores ranging between 98% to 100% at a 50% bounding box overlap threshold. Notably, when scrutinized under higher threshold values, YOLOX emerges as the superior model, exhibiting heightened accuracy in bounding box predictions with an 84% AP score at an 80% overlap threshold, compared to 72.48% AP for YOLOv4. This remarkable performance, particularly at the standard 50% overlap threshold, signifies a significant stride towards meeting the stringent clinical requisites for integrating AI-based solutions into clinical EEG analysis workflows. [ABSTRACT FROM AUTHOR]

Attention is a cognitive process that is essential for human performance. Human development frequently depends on attention; however, this topic still requires additional research. Recently, EEG brain waves have been utilised to identify a person's attention states. This work aims to review numerous machine learning algorithms to analyse the Electroencephalographic (EEG) data to recognise human attention. Various machine learning approaches for the analysis of emotional states with EEG data were reviewed. Moreover, the analysis includes the performance achieved in various works; their benefits and disadvantages are reviewed. An EEG data processing pipeline and a review of human attention recognition are developed to evaluate the performance of various machine learning techniques. According to the analysis, the neural network framework achieves 99.81% accuracy. The results serve as a design framework for future systems using EEG data on brain activity to monitor people's health. [ABSTRACT FROM AUTHOR]

Background: Kickboxing is a popular striking combat sport, and K-1 is a type of kickboxing. Direct head blows can cause significant long-term injury and affect brain wave activity. Objectives: We aim to compare the changes in brain wave activities of fighters during a K-1 kickboxing contest to those in a control group, who were striking a punching bag and were not hit by another K-1 athlete. Methods: A total of 100 professional Polish K-1 kickboxers were split evenly into experimental (n = 50, age 25.5 ± 4.63 years) and control (n = 50, age 26.6 ± 5.22 years) groups. We used quantitative electroencephalography (QEEG) to assess the spectrum of brain wave activity (delta, theta, alpha, sensorimotor rhythm (SMR), beta-1 and beta-2) before and after an intervention (experimental: K-1 contest, control: simulated contest), with eyes open and then closed. The number of direct blows to the head was also recorded for all bouts. Comparative and statistical analyses between selected variables were performed. Results: K-1 fighters showed elevated baseline brain activity for the entire delta band (p < 0.001). There was significant variation in brain activity among the experimental group following the intervention and compared with the control group for all wave types (p < 0.001). No significant variation in activity was found in the control group. The number of direct head blows was positively correlated with brain activity, at delta and beta-2 wave frequencies. Conclusions: K-1 kickboxing is associated with detectable changes in brain wave activity. It is presently unclear what the long-term effects of these changes in brain wave activities are, and longitudinal studies are necessary to study the brain health of kickboxers. [ABSTRACT FROM AUTHOR]

Previously, the electroencephalogram (EEG) recorded while listening to repetition of a Sanskrit monosyllable (i.e., OM) was not significantly different from the EEG recorded during control periods. In the present study, fifteen adult volunteers from northern India (all male, average age 25.0 ± 4.9 years) were assessed using monopolar scalp EEG recordings before, during and after (i) silent repetition of a monosyllable (OM), (ii) silent repetition of 'one' and (iii) quiet rest. In (i), (ii) and (iii) breathing was not consciously modified. The participants had an average of five years' experience of focused meditation (dharana), involving awareness directed to the breath. Participants had no prior experience of silent repetition of either 'OM' or 'one'. They were Indian Hindus for whom OM has special spiritual significance, whereas 'one' does not. The sessions were on separate days in random order. The absolute and relative energy of theta waves in the EEG recorded at Pz-A2 increased significantly during repetition of OM compared to the preceding state. There were no changes during silent repetition of 'one' or during quiet rest. Increased energy in the theta band while awake reflects a state of relaxation devoid of effort. Hence, during silent repetition of OM alone there were changes suggestive of effortless relaxation. [ABSTRACT FROM AUTHOR]

Neural entrainment has become a popular technique to non-invasively manipulate brain rhythms via external, periodic stimulation. However, there is still debate regarding its underlying mechanisms and effects on brain activity. Here, we used EEG recordings during a visual entrainment paradigm to assess characteristic changes in the spectral content of EEG signals due to entrainment. Our results demonstrate that entrainment not only increases synchrony between neural oscillations and the entraining stimulus but also elicits previously unreported spectral tuning effects and long-lasting after-effects. These findings offer compelling evidence for the presence of dedicated, flexible, and adaptive mechanisms for neural entrainment, which may have key roles in adjusting the sensitivity and dynamic range of brain oscillators in response to environmental temporal structures. [ABSTRACT FROM AUTHOR]

Objective: This systematic review and meta-analysis of randomized-clinical trials aims to analyze the effect of interventions incorporating surface neurofeedback techniques on self-perceived sleep quality and insomnia in patients with or without sleep disturbances. Methods: The review was completed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement and was deposited in the Prospero international prospective registry of systematic reviews (CRD42024528401). Seven clinical trials with different main outcomes but with pre-post intervention records of self-perceived sleep quality or insomnia symptoms assessed by questionnaires met our inclusion criteria, including a publication date within the last 10 years. Five trials investigated sleep quality through scores on the Pittsburgh sleep quality Index (PSQI) and three trials signs of insomnia severity assessed with validated scales. The methodological quality of the included studies was assessed using the Cochrane Collaboration's tool for assessing the risk of bias and showed a high quality of them. Results: A total of 5 studies that evaluated sleep quality with the PSQI total score were included in the meta-analysis. The results revealed that control conditions succeeded in improving PSQI-assessed sleep quality more than the analyzed Neurofeedback interventions (PSQI total score 0.57; 95% CI 0.13 to 1.01; p = 0.01). On the other hand, a total of 3 studies that evaluated insomnia severity with various insomnia scales were included in the meta-analysis The results revealed that neither the NF interventions nor the control conditions show a favorable outcome relative to each other (−0.13; 95% CI −0.44 to 0.18; p = 0.41). Conclusion: The interventions studied mostly apply a neurofeedback training protocol based on maintaining alpha waves in a range between 8 and 12 Hz, with electrode positioning in the frontal area or in the sensorimotor cortex and with a number of neurofeedback sessions ranging from 8 to 20 sessions. The meta-analysis showed that interventions incorporating surface neurofeedback do not produce additional benefits in self-perception of sleep quality or insomnia compared to a wide variety of control conditions including cognitive behavioral treatment or other biofeedback modalities. Systematic review registration: PROSPERO – International prospective register of systematic reviews – CRD42024528401 https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=528401. [ABSTRACT FROM AUTHOR]

To transform continuous speech into words, the human brain must resolve variability across utterances in intonation, speech rate, volume, accents and so on. A promising approach to explaining this process has been to model electroencephalogram (EEG) recordings of brain responses to speech. Contemporary models typically invoke context invariant speech categories (e.g. phonemes) as an intermediary representational stage between sounds and words. However, such models may not capture the complete picture because they do not model the brain mechanism that categorizes sounds and consequently may overlook associated neural representations. By providing end-to-end accounts of speech-to-text transformation, new deep-learning systems could enable more complete brain models. We model EEG recordings of audiobook comprehension with the deep-learning speech recognition system Whisper. We find that (1) Whisper provides a self-contained EEG model of an intermediary representational stage that reflects elements of prelexical and lexical representation and prediction; (2) EEG modeling is more accurate when informed by 5-10s of speech context, which traditional context invariant categorical models do not encode; (3) Deep Whisper layers encoding linguistic structure were more accurate EEG models of selectively attended speech in two-speaker "cocktail party" listening conditions than early layers encoding acoustics. No such layer depth advantage was observed for unattended speech, consistent with a more superficial level of linguistic processing in the brain. Author summary: Most people effortlessly can understand different speakers with distinct voices and accents, no matter whether they whisper, shout or are happy or sad. This effortlessness belies the remarkable computational challenge that our brains solve to transform such variable speech sounds into words. It is believed that our brains deal with this variability by categorizing speech sounds into a flow of phonemes and/or syllable units that are consistent, no matter how a word is spoken. Compelling supporting evidence has come from electrophysiological recordings of brain activity–colloquially known as brain waves—taken as people listen to speech. Scientists have trained computational models to predict brain wave fluctuations that correlate with sequences of phoneme categories. However, modeling only phoneme categories may miss key stages in the recognition process, including how sounds are mapped to phonemes, and phonemes to words. New deep learning speech models that are trained to recognize a diverse range of speech and speakers may offer new opportunities to provide more complete accounts of brain activity. This article reveals that these models indeed predict hitherto unexplained fluctuations in speech brain waves that reflect elements of sub-words and words, and shows that fluctuations are context sensitive, which may reflect the brain anticipating upcoming speech. [ABSTRACT FROM AUTHOR]

Background: Deep brain stimulation (DBS) targeting the subthalamic nucleus (STN) has emerged as a potent treatment for alleviating motor symptoms in Parkinson's disease (PD). Despite its effectiveness, the impact of high frequency STN-DBS on cerebellar oscillations remains unclear, posing an intriguing challenge for neural modulation. Given the direct and indirect connections between the STN and cerebellum, we investigated whether STN-DBS affects cerebellar oscillations. Objective: To observe the effects of STN-DBS on cerebellar oscillations in patients with PD. Methods: We recruited 15 PD patients receiving STN-DBS. Electroencephalographic (EEG) signals were recorded from cerebellar regions during resting-state conditions in both the OFF-DBS and STN-DBS conditions. Our analyses centered on spectral features, particularly theta and beta oscillations, guided by prior research and correlation tests to investigate the relationship between oscillatory changes and motor symptom severity. Results: In the mid-cerebellar (Cbz) region, we observed a significant increase in the relative power in all frequency bands, including theta and beta oscillations during STN-DBS, showing the global effect of DBS. Importantly, the correlation results indicated significant associations between mid-cerebellar (Cbz) beta power during the OFF condition and motor severity, which were not evident during STN-DBS. Interestingly, correlations between beta power and motor severity were not observed at the mid-occipital (Oz) and mid-frontal (Cz) regions. Notably, signal similarity analyses demonstrated no evidence of volume conduction effects between the mid-cerebellar (Cbz) and nearby mid-occipital (Oz) regions. Conclusions: While these findings provide valuable insights into the complex interplay between STN-DBS and neural oscillations, further research is essential to decipher their precise functional significance and clinical implications. Understanding these intricacies may contribute to the optimization of DBS therapies for PD. Plain Language Summary: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for reducing motor symptoms in Parkinson's disease (PD). However, its effects on brain activity, specifically in the cerebellum, are not well understood. This study aimed to investigate how STN-DBS affects cerebellar brain waves in PD patients. We recruited 15 PD patients undergoing STN-DBS and recorded their brain activity including cerebellar region using EEG. We compared the brain wave patterns during periods when the DBS was turned OFF and when it was turned ON, focusing on specific brain wave frequencies (theta and beta). The results showed a significant increase in brain wave power across all frequencies in the mid-cerebellar region during STN-DBS. Additionally, there was a strong link between beta power in the cerebellum and motor symptom severity when DBS was OFF, which was not present when DBS was ON. This relationship was specific to the cerebellum and not found in other brain regions. The findings suggest that STN-DBS significantly alters cerebellar brain activity and that these changes are related to improvements in motor symptoms. However, more research is needed to fully understand the functional significance and potential clinical applications of these findings for optimizing DBS treatment in PD patients. [ABSTRACT FROM AUTHOR]

Research on the effects of exercise on cortical activity has grown significantly, with a predominant focus on the technique of electroencephalography (EEG). Technological advancements in EEG render this technique suitable for studies that explore human movement. There is a noticeable gap in the literature concerning how EEG-recorded cortical oscillatory activity is affected by exercise. Accordingly, the aim of this review was to summarise studies that investigated the effects of exercise on brain activity using EEG. This was a systematic review of 47 studies, predicated on PRISMA guidelines. The results were, on the whole, equivocal in nature, with the exception that activity in the alpha and beta frequency bands increased both during and post-exercise across the frontal, central, and limbic regions. The reported increases could be attributed to a variety of neural processes, such as neuro-connectivity, cortical inhibition, and sensory feedback loops. Future researchers need to give careful consideration to exercise parameters (e.g. mode of exercise, intensity, duration) to facilitate their interpretation of experimental data. The present findings support the notion that exercise alters alpha and beta neural oscillations but more work is needed to provide a fuller picture of the brain processes that manifest during exercise. [ABSTRACT FROM AUTHOR]

Introduction: Substance abuse is a major public health problem. In recent years, ketamine, which is a parenteral anesthetic, has been consumed increasingly as an illicit drug together with alcohol, although little is known of how this association alters brain activity. The present study investigated the influence of progressive doses of ketamine, associated with alcohol, on electrophysiological activity. Methods: For this, 72 late-adolescent (8–10-week-old) male Wistar rats received either ketamine only, at low (10 mg/kg), intermediate (20 mg/kg) or high (30 mg/kg) doses via intraperitoneal injection, or alcohol (2 mL/100 g) via oral gavage followed by ketamine (at low, intermediate, and high doses). Electroencephalograms (EEG) and electromyographic recordings were obtained 5 min after the final application of the drug. Results: When administered alone, ketamine resulted in an increase in delta, theta, beta, and gamma brainwaves, with a more pronounced effect being detected at the highest dose (30 mg/kg) in the case of the delta, beta, and gamma waves. The amplitude of the alpha brainwaves was reduced at all doses of ketamine, but less intensively at the highest dose. When administered alone, alcohol reduced all the brainwaves, with the reduction in the alpha waves being exacerbated by ketamine at all doses, and that of the theta and beta waves being boosted at the lowest dose. The intermediate dose of ketamine (20 mg/kg) reverted the alcohol-induced reduction in the theta and gamma waves, whereas the high dose increased delta, theta, beta, and gamma bandpower. Discussion: Overall, then, while ketamine enhances the depressant effects of alcohol on the alpha brainwave at all doses, a low dose intensified this effect on the theta and beta 175 waves, whereas a high dose produces neuronal hyperexcitability in the theta and 176 gamma bandpower. [ABSTRACT FROM AUTHOR]

Our understanding of brain circuit operations and disorders has rapidly outpaced our ability to intervene and restore them. Developing technologies that can precisely interface with any brain region and circuit may combine diagnostics with therapeutic intervention, expediting personalised brain medicine. Transcranial ultrasound stimulation (TUS) is a promising noninvasive solution to this challenge, offering focal precision and scalability. By exploiting the biomechanics of pressure waves on brain tissue, TUS enables multi-site targeted neuromodulation across distributed circuits in the cortex and deeper areas alike. In this Essay, we explore the emergent evidence that TUS can functionally test and modify dysfunctional regions, effectively serving as a search and rescue tool for the brain. We define the challenges and opportunities faced by TUS as it moves towards greater target precision and integration with advanced brain monitoring and interventional technology. Finally, we propose a roadmap for the evolution of TUS as it progresses from a research tool to a clinically validated therapeutic for brain disorders. Transcranial ultrasound stimulation (TUS) is a promising non-invasive solution to interface with specific brain regions with high precision to combine diagnostics with therapeutic interventions. This Essay explores the challenges and proposes a roadmap for the evolution of TUS from a research tool to a clinically validated therapeutic. [ABSTRACT FROM AUTHOR]

Introduction: Substance abuse is a major public health problem. In recent years, ketamine, which is a parenteral anesthetic, has been consumed increasingly as an illicit drug together with alcohol, although little is known of how this association alters brain activity. The present study investigated the influence of progressive doses of ketamine, associated with alcohol, on electrophysiological activity. Methods: For this, 72 late-adolescent (8–10-week-old) male Wistar rats received either ketamine only, at low (10 mg/kg), intermediate (20 mg/kg) or high (30 mg/kg) doses via intraperitoneal injection, or alcohol (2 mL/100 g) via oral gavage followed by ketamine (at low, intermediate, and high doses). Electroencephalograms (EEG) and electromyographic recordings were obtained 5 min after the final application of the drug. Results: When administered alone, ketamine resulted in an increase in delta, theta, beta, and gamma brainwaves, with a more pronounced effect being detected at the highest dose (30 mg/kg) in the case of the delta, beta, and gamma waves. The amplitude of the alpha brainwaves was reduced at all doses of ketamine, but less intensively at the highest dose. When administered alone, alcohol reduced all the brainwaves, with the reduction in the alpha waves being exacerbated by ketamine at all doses, and that of the theta and beta waves being boosted at the lowest dose. The intermediate dose of ketamine (20 mg/kg) reverted the alcohol-induced reduction in the theta and gamma waves, whereas the high dose increased delta, theta, beta, and gamma bandpower. Discussion: Overall, then, while ketamine enhances the depressant effects of alcohol on the alpha brainwave at all doses, a low dose intensified this effect on the theta and beta 175 waves, whereas a high dose produces neuronal hyperexcitability in the theta and 176 gamma bandpower. [ABSTRACT FROM AUTHOR]

Changes in neurovascular unit components and their interactions play a crucial role in epileptogenesis and the pathological process of epilepsy. Currently, there is a lack of animal models that can accurately reflect the etiological impact of cerebrovascular lesions on epilepsy. In this study, we constructed cyclindependent kinase 5 conditional knockout mice in Cspg4 (pericyte marker)- positive cells using the Cre-LoxP system. The results revealed that this strain of mice exhibited significant seizure behaviors and epileptiform brain waves, loss of hippocampal and amygdala neurons, astrogliosis, decreased pericyte coverage, and reduced AQP4 polar distribution. Herein, we have developed a novel mouse model of spontaneous epilepsy, providing a critical animal model for studying the involvement of neurovascular unit factors in the development and progression of epilepsy. [ABSTRACT FROM AUTHOR]

The paper introduces a parametric resonance model for characterizing some features of the brain's electrical activity. This activity is assumed to be a fundamental aspect of brain functionality underpinning functions from basic sensory processing to complex cognitive operations such as memory, reasoning, and emotion. A pivotal element of the proposed parametric model is neuron synchronization which is crucial for generating detectable brain waves. The analysis of the frequency content of brain waves, categorized as delta (0÷4 Hz), theta (4÷7 Hz), alpha (8÷12 Hz), beta (13÷30 Hz), and gamma (30÷100 Hz) reveals, notably, that the mean frequency of each of these brain wave classes is, in sequence, approximately the double of that of the previous one. Based on this observation, the proposed parametric resonance model suggests a cascade of amplification effects. Following the proposed model, in the transition from wakefulness to sleep, the brain wave bands are energized at double frequency by higher frequency neighboring bands; on the contrary, in the sleep to awake transition, brain waves are energized at a half frequency by their lower frequency neighbor waves. Finally, the trend of increasing amplitude values from higher to lower frequencies lends empirical support to the parametric resonant brain model validity. [ABSTRACT FROM AUTHOR]

Emotions significantly impact human thinking, judgment, health, and communication. EEG-based emotion detection has advanced with the use of Brain-Computer Interface (BCI) technology, proving more effective than other physiological data. Despite progress in affective computing, emotion recognition remains a challenge. However, it's increasingly common in brain–machine interfaces, and research shows EEG brain waves are valuable in identifying emotional states. This research introduces a novel automated system for emotion recognition using deep learning techniques on EEG data collected from the GAMEEMO dataset, where participants played emotional assessment games. The system is designed to identify four emotions experienced during gameplay. The proposed model, called EEGER, was trained exclusively on EEG signals and demonstrated a 99.99% accuracy with minimal computational time. Key to its efficiency is the use of LSTM (Long Short-Term Memory) classifiers, which simplify the process by automatically extracting relevant features. The system was tested across different learning rates and epoch values, showing that 10 epochs with a learning rate of 0.0001 were sufficient to achieve the best accuracy. EEGER was also compared with other methods like K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Adaptive Boosting (AdaBoost), outperforming them in both accuracy and efficiency. These findings suggest that EEGER offers a promising new approach to EEG-based emotion recognition, optimizing performance with lower complexity and computation time. [ABSTRACT FROM AUTHOR]

This study aimed to develop wearable robotic gloves that can provide comfort to logistics and transportation workers while providing hand assistance and addressing accumulated muscle fatigue caused by repetitive manual handling tasks. Two types of gloves, rivet-type and pouch-type were developed and evaluated using the nine-hole peg test (NHPT) and grip strength tests. Results showed rivet-type gloves had better dexterity, with an 8.14% faster NHPT time, and maintained 85% grip strength compared to 80% for pouch-type gloves, even after fatigue exercises. Wearability tests were conducted by measuring clothing pressure and brainwaves. In contrast to the functional experimental results, the pouch-type gloves exhibited significant decreases in clothing pressure during actuation. According to the difference in brain waves in the frontal lobe region, the beta and gamma waves observed when wearing pouch-type gloves were lower than those of rivet-type gloves, and pouch-type gloves exhibited higher alpha-wave results than rivet-type gloves. These results imply that participants were in a comparatively relaxed state when wearing pouch-type gloves. A wearer's evaluation survey on the overall design, function, comfort, safety, ease of use, usefulness, and use intention was conducted. In summary, the rivet-type gloves exhibited better results in hand performance while the pouch-type gloves showed advantages in wearability, suggesting a tradeoff relationship between force transmission and wearer comfort. The results of this study aimed to improve the work efficiency at logistics and transportation industry sites and help improve the welfare of workers. [ABSTRACT FROM AUTHOR]

Hypnotherapy is a form of psychological treatment that utilizes hypnosis to assist in addressing specific mental and physical ailments. It can also be employed for altering habits. Hypnotherapy operates by inducing a hypnotic state in the subject, lowering brain waves to alpha-theta levels for relaxation and enhanced acceptance of suggestions in the subconscious. The aim is to improve psychological issues, emotions, and conduct. The study was carried out on two patients at the Lee Chen Thung Primary Clinic in Jakarta, Indonesia. Sampling was conducted by adhering to specific criteria for selection and administering the GERD-Q questionnaire. The patients included in the study had to have GERD-Q scores higher than 7 and frequently use PPIs for their stomach issues. Management of GERD can be done with drug therapy or pharmacology and non-pharmacology therapy. One of the non-pharmacology therapies that can be given is hypnotherapy. Patients who satisfied the criteria were provided with hypnotherapy in order to alleviate their heartburn symptoms. The process of hypnotherapy can help the body to stimulate the release of endorphins. Endorphins act as the body's innate pain relievers. Following hypnotherapy, patients underwent evaluation for 1 week using numeric rating scale. Both patients experienced a decrease in pain levels following hypnotherapy. [ABSTRACT FROM AUTHOR]

Background: Mental health issues are increasingly prominent worldwide, posing significant threats to patients and deeply affecting their families and social relationships. Traditional diagnostic methods are subjective and delayed, indicating the need for an objective and effective early diagnosis method. Methods: To this end, this paper proposes a lightweight detection method for multi-mental disorders with fewer data sources, aiming to improve diagnostic procedures and enable early patient detection. First, the proposed method takes Electroencephalography (EEG) signals as sources, acquires brain rhythms through Discrete Wavelet Decomposition (DWT), and extracts their approximate entropy, fuzzy entropy, permutation entropy, and sample entropy to establish the entropy-based matrix. Then, six kinds of conventional machine learning classifiers, including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Naive Bayes (NB), Generalized Additive Model (GAM), Linear Discriminant Analysis (LDA), and Decision Tree (DT), are adopted for the entropy-based matrix to achieve the detection task. Their performances are assessed by accuracy, sensitivity, specificity, and F1-score. Concerning these experiments, three public datasets of schizophrenia, epilepsy, and depression are utilized for method validation. Results: The analysis of the results from these datasets identifies the representative single-channel signals (schizophrenia: O1, epilepsy: F3, depression: O2), satisfying classification accuracies (88.10%, 75.47%, and 89.92%, respectively) with minimal input. Conclusions: Such performances are impressive when considering fewer data sources as a concern, which also improves the interpretability of the entropy features in EEG, providing a reliable detection approach for multi-mental disorders and advancing insights into their underlying mechanisms and pathological states. [ABSTRACT FROM AUTHOR]

Purpose: To monitor changes in mood, cognitive function, brain electrical activity, and circulating kynurenine pathway metabolites in response to a 3-wk severe physical activity (PA) restriction, followed by 3 wk of resumed activity adding resistance and high-intensity interval exercise training. Methods: Twenty healthy participants (14 males, 6 females; 25.4 ± 5.2 yr) underwent 3 wk of limited PA using forearm crutches with one leg suspended (INACT) and then 3 wk of resumed activity plus supervised resistance and high-intensity interval training sessions (ACT, three to six sessions per week). At baseline, after INACT, and then after ACT, venous blood was sampled for analysis of major kynurenine pathway metabolites, a short version of the International Physical Activity Questionnaire, Hospital Anxiety and Depression Scale (HADS) and Profile of Mood States (POMS) questionnaires were completed, and cognitive tests with electroencephalography were performed. Results: During INACT, the depression score on the HADS scale tended to increase (3.5 to 6.8; P = 0.065), whereas it was reduced with ACT compared with after INACT (2.8; P = 0.022). On the POMS scale, depression, fatigue, and confusion increased within INACT (P < 0.05). Notably, subjects exhibited considerable variability, and those experiencing depression symptoms recorded by the HADS scale (n = 4) displayed distinct mood disturbances on POMS. All HADS and POMS scores were fully restored to baseline with ACT. Neither INACT nor ACT induced significant changes in cognition, brain electrical activity, or kynurenine pathway metabolites (P > 0.05). Conclusions: Although young healthy individuals with 3 wk of severely restricted PA do not undergo changes in circulating kynurenine pathway metabolites, cognitive performance, and brain electrical activity, their mood response is quite variable, and depression develops in some. Three weeks of resuming mobility plus exercise training reversed the mood profile. [ABSTRACT FROM AUTHOR]

Background: Musculoskeletal disorders such as cervicogenic headaches present with suboccipital muscle hypertonicity and trigger points. One manual therapy intervention commonly used to target the suboccipital muscles is the suboccipital release technique, previously related to positive systemic effects. Therefore, this study aimed to determine the immediate and short-term effects of the Suboccipital Release Technique (SRT) on brainwave activity in a subgroup of healthy individuals. Methods: Data were collected from 37 subjects (20 females and 17 males, with a mean age of 24.5). While supine, the subjects underwent a head hold followed by suboccipital release. A total of four 15 s electroencephalogram (EEG) measurements were taken and a Global Rating of Change Scale was used to assess self-perception. Results: There was a statistically significant difference (p < 0.005) in various band waves under the following electrodes: AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, and FC6. An 8-point range in the Global Rating of Change Scores with a mean score of 1.649 (SD = 1.719 and SE = 0.283) supported the hypothesis of a self-perceived benefit from the intervention. Conclusions: The results of this study indicate that the suboccipital release technique significantly affects brain wave activity throughout different brain regions. This change is likely not the result of any placebo effect and correlates highly with the subject's self-perception of a change following the intervention. These findings support the clinical use of the suboccipital release technique when a centralized effect is desired. [ABSTRACT FROM AUTHOR]

Physiological oscillations, such as those involved in brain activity, heartbeat, and respiration, display inherent rhythmicity across various timescales. However, adaptive behavior arises from the interaction between these intrinsic rhythms and external environmental cues. In this study, we used multimodal neurophysiological recordings, simultaneously capturing signals from the central and autonomic nervous systems (CNS and ANS), to explore the dynamics of brain and body rhythms in response to rhythmic auditory stimulation across three conditions: baseline (no auditory stimulation), passive auditory processing, and active auditory processing (discrimination task). Our findings demonstrate that active engagement with auditory stimulation synchronizes both CNS and ANS rhythms with the external rhythm, unlike passive and baseline conditions, as evidenced by power spectral density (PSD) and coherence analyses. Importantly, phase angle analysis revealed a consistent alignment across participants between their physiological oscillatory phases at stimulus or response onsets. This alignment was associated with reaction times, suggesting that certain phases of physiological oscillations are spontaneously prioritized across individuals due to their adaptive role in sensorimotor behavior. These results highlight the intricate interplay between CNS and ANS rhythms in optimizing sensorimotor responses to environmental demands, suggesting a potential mechanism of embodied predictive processing. [ABSTRACT FROM AUTHOR]

The human brain exhibits both oscillatory and aperiodic, or 1/f, activity. Although a large body of research has focused on the relationship between brain rhythms and sensory processes, aperiodic activity has often been overlooked as functionally irrelevant. Prompted by recent findings linking aperiodic activity to the balance between neural excitation and inhibition, we investigated its effects on the temporal resolution of perception. We recorded electroencephalography (EEG) from participants (both sexes) during the resting state and a task in which they detected the presence of two flashes separated by variable interstimulus intervals. Two-flash discrimination accuracy typically follows a sigmoid function whose steepness reflects perceptual variability or inconsistent integration/segregation of the stimuli. We found that individual differences in the steepness of the psychometric function correlated with EEG aperiodic exponents over posterior scalp sites. In other words, participants with flatter EEG spectra (i.e., greater neural excitation) exhibited increased sensory noise, resulting in shallower psychometric curves. Our finding suggests that aperiodic EEG is linked to sensory integration processes usually attributed to the rhythmic inhibition of neural oscillations. Overall, this correspondence between aperiodic neural excitation and behavioral measures of sensory noise provides a more comprehensive explanation of the relationship between brain activity and sensory integration and represents an important extension to theories of how the brain samples sensory input over time. [ABSTRACT FROM AUTHOR]

This study measured and compared the psychophysiological and psychological differences in the responses of men and women to olfactory stimuli from herbal plants. A total of 30 adult participants (mean age, 27.4 years; SD, ±8.97 years; 15 men and 15 women) were included and five different herbs were used: lavender, rosemary, sage, apple mint, and pelargonium. During olfactory activity, participants smelled each herb for 90 seconds while relying solely on their sense of smell and electroencephalography was used to measure brain wave changes. Subsequently, participants’ emotional states were assessed using the semantic differential method (SDM). The results indicated significant differences in the relative alpha, relative slow alpha, and relative low alpha frequencies in the prefrontal lobe (Fp1 and Fp2) for both genders (P < 0.05). Significant gender differences were observed in the relative beta, relative middle beta, ratio of sensorimotor rhythms-mid beta to theta frequencies in the occipital lobe (O1 and O2) (P < 0.05). The SDM results showed significant natural emotional responses in both genders after olfactory stimulation with herbal plants. Furthermore, compared with men, women exhibited more natural emotions to sage, apple mint, and pelargonium olfactory stimulation. These findings affirm the calming effects of olfactory stimulation with herbal plants for both genders, thus underscoring gender differences in preferences and psychological responses. [ABSTRACT FROM AUTHOR]

Mu rhythm (∼8–12 Hz) in the somatosensory cortex has traditionally been linked with doing and seeing motor activities. Here, we aimed to learn how the medium (physical or screened) in which motor actions are seen could impact on that specific brain rhythm. To do so, we presented to 40 participants the very same narrative content both in a one-shot movie with no cuts and in a real theatrical performance. We recorded subjects' brain activities with electroencephalographic (EEG) procedures, and analyzed Mu rhythm present in left (C3) and right (C4) somatosensory areas in relation to the 24 motor activities included in each visual stimulus (screen vs. reality) (24 motor and grasping actions x 40 participants x 2 conditions = 1920 trials). We found lower Mu spectral power in the somatosensory area after the onset of the motor actions in real performance than on-screened content, more pronounced in the left hemisphere. In our results, the sensorimotor Mu-ERD (event-related desynchronization) was stronger during the real-world observation compared to screen observation. This could be relevant in research areas where the somatosensory cortex is important, such as online learning, virtual reality, or brain-computer interfaces. [ABSTRACT FROM AUTHOR]

Chronic pain due to peripheral neuropathy can lead to sleep disorders that significantly worsen the patient's quality of life. Previously, we conducted brain wave measurements in a rat model of neuropathic pain and identified its potential as a model for sleep disorders associated with chronic pain (reported). In this study, we quantified melatonin secretion and assessed its circadian rhythm in a rat model of pain-induced sleep disorder. To create a model of chronic constriction injury (CCI), rats were loosely tied around the sciatic nerve, with approximately 1 mm spacing, 14 days before the experiment. Rats with no ties around the sciatic nerve were used as controls. Electroencephalograms and electromyograms were recorded for 3 days, and the episodes of waking, REM sleep, and non-REM sleep were compared between the groups. The samples for microanalysis were collected every 30 min and used for melatonin analysis. Compared to the control group, the CCI model group exhibited an increase in wake episodes and a decrease in non-REM sleep episodes. Analysis of the area under the curve of melatonin secretion revealed a significant increase in melatonin secretion and a loss of circadian rhythm in the CCI model group. Melatonin secretion markedly increased accompanied by loss of circadian rhythm in a rat model of CCI. Further studies investigating the causal relationship between neuropathic pain and melatonin secretion are warranted. [ABSTRACT FROM AUTHOR]

Chronobiological rhythms, such as the circadian rhythm, have long been linked to neurological disorders, but it is currently unknown how pathological processes affect the expression of biological rhythms in the brain. Here, we use the unique opportunity of long-term, continuous intracranially recorded EEG from 38 patients (totalling 6338 hours) to delineate circadian (daily) and ultradian (minute to hourly) rhythms in different brain regions. We show that functional circadian and ultradian rhythms are diminished in pathological tissue, independent of regional variations. We further demonstrate that these diminished rhythms are persistent in time, regardless of load or occurrence of pathological events. These findings provide evidence that brain pathology is functionally associated with persistently diminished chronobiological rhythms in vivo in humans, independent of regional variations or pathological events. Future work interacting with, and restoring, these modulatory chronobiological rhythms may allow for novel therapies. The role of long-term fluctuations in intracranial EEG signals in epilepsy is currently unclear. Here the authors show that the circadian rhythm as well as various ultradian rhythms are diminished in brain regions thought to be central in epilepsy. [ABSTRACT FROM AUTHOR]

When loaded with difficulties in fulfilling daily requirements, a lot of people in today's world experience an emotional pressure known as stress. Stress that lasts for a short duration of time has more advantages as they are good for mental health. But, the persistence of stress for a long duration of time may lead to serious health impacts in individuals, such as high blood pressure, cardiovascular disease, stroke and so on. Long-term stress, if unidentified and not treated, may also result in personality disorder, depression and anxiety. The initial detection of stress has become more important to prevent the health issues that arise due to stress. Detection of stress based on brain signals for analysing the emotion in humans leads to accurate detection outcomes. Using EEG-based detection systems and disease, disability and disorders can be identified from the brain by utilising the brain waves. Sentiment Analysis (SA) is helpful in identifying the emotions and mental stress in the human brain. So, a system to accurately and precisely detect depression in human based on their emotion through the utilisation of SA is of high necessity. The development of a reliable and precise Emotion and Stress Recognition (ESR) system in order to detect depression in real-time using deep learning techniques with the aid of Electroencephalography (EEG) signal-based SA is carried out in this paper. The essentials needed for performing stress and emotion detection are gathered initially from benchmark databases. Next, the pre-processing procedures, like the removal of artifacts from the gathered EEG signal, are carried out on the implemented model. The extraction of the spectral attributes is carried out from the pre- processed. The extracted spectral features are considered the first set of features. Then, with the aid of a Conditional Variational Autoencoder (CVA), the deep features are extracted from the pre-processed signals forming a second set of features. The weights are optimised using the Adaptive Egret Swarm Optimisation Algorithm (AESOA) so that the weighted fused features are obtained from these two sets of extracted features. Then, a Cascaded Deep Temporal Convolution Network with Attention Mechanism (CDTCN-AM) is used to recognise stress and emotion. The validation of the results from the developed stress and emotion recognition approach is carried out against traditional models in order to showcase the effectiveness of the suggested approach. [ABSTRACT FROM AUTHOR]

Background: Yoga breathing has been shown to enhance neurocognitive function and positive emotions by increasing electrical power in several frequency bands and synchronizing interhemispheric brain waves. The current study examined the immediate impact of practicing Kapalabhati (KBH) on the electrical activity of the brain. Methods: Thirty-six individuals who met the inclusion and exclusion criteria and ranged in age from 18 to 25 were randomly assigned, 1:1, to the KBH (n = 18) and breath awareness (BAW) (n = 18) groups. Before data collection, both groups received their respective practices for 10 min each day for a total of 15 days. The brain’s electrical activities were assessed using 128-channel EEG recording. The electrodes were placed on their scalps according to the international 10-10 system, ensuring optimal coverage of different brain regions. The EEG signals were amplified, digitized, and stored for offline analysis. Results: The EEG data showed that the practice of KBH significantly increased alpha waves in the frontal and temporal regions. Moreover, gamma waves increased significantly in the frontal, temporal, and occipital regions after the practice of KBH when compared with BAW. Conclusion: The results suggest the involvement of frontal and temporal regions, which highlights the importance of KBH in enhancing higher-order cognitive processes. These results provide valuable insights and support for the use of KBH as a potential intervention for individuals seeking to enhance their cognitive abilities.

Background: Quantitative Electroencephalography (QEEG) is a tool helping better understand the electrical activity of the brain and a non-invasive method to assess cortical activity. To date, the brain activity of patients with chronic patellofemoral pain (PFP) has not been investigated.Objective: The current study aimed to investigate the effect of PFP on higher levels of the central nervous system by assessing the correlation between QEEG and modified excursion balance test (mSEBT) in patients with PFP.Material and Methods: Twenty-two patients with chronic PFP participated in this observational study. Their cortical electrical activity was recorded in a resting state with their eyes open, via a 32-channel QEEG. C3, C4, and Cz were considered as regions of interest. In addition to QEEG, the balance performance of the participants was evaluated via mSEBT.Results: The obtained findings revealed a negative and moderate to high correlation between theta absolute power and posteromedial direction of mSEBT in C4 (P: 0.000, r: -0.68), Cz (P: 0.001, r: -0.66), and C3 (P 0.000, r: -0.70). Additionally, a significantly close correlation is between alpha absolute power in C3 (P: 0.001, r: -0.70), C4 (P: 0.000, r: -0.71), and Cz (P: 0.000, r: -0.74) and the posteromedial direction of mSEBT. No significant correlations were between the other two directions of mSEBT, alpha, and theta. Conclusion: According to our results, balance impairment in patients with chronic PFP correlated with their QEEG neurodynamics. Moreover, our findings demonstrated the efficiency of QEEG as a neuromodulation method for patients with PFP.

Circadian, infradian, and seasonal changes in steroid hormone secretion have been tied to changes in brain volume in several mammalian species. However, the relationship between circadian changes in steroid hormone production and rhythmic changes in brain morphology in humans is largely unknown. Here, we examined the relationship between diurnal fluctuations in steroid hormones and multiscale brain morphology in a precision imaging study of a male who completed 40 MRI and serological assessments at 7 A.M. and 8 P.M. over the course of a month, targeting hormone concentrations at their peak and nadir. Diurnal fluctuations in steroid hormones were tied to pronounced changes in global and regional brain morphology. From morning to evening, total brain volume, gray matter volume, and cortical thickness decreased, coincident with decreases in steroid hormone concentrations (testosterone, estradiol, and cortisol). In parallel, cerebrospinal fluid and ventricle size increased from A.M. to P.M. Global changes were driven by decreases within the occipital and parietal cortices. These findings highlight natural rhythms in brain morphology that keep time with the diurnal ebb and flow of steroid hormones. [ABSTRACT FROM AUTHOR]

This document is a summary of a collection of articles on the roles of temporal precision, synchronization, and neural oscillations in brain function. The articles discuss innovative methodologies for analyzing brain oscillations and their importance in understanding neural circuit dynamics and brain functions. They also highlight the relevance of neural oscillations in speech and music processing, as well as in neurodevelopmental disorders like dyslexia and stuttering. The articles offer valuable perspectives for both research and clinical applications. [Extracted from the article]

A compact description of the frequency structure and topography of human alpha-band rhythms is obtained by use of the first four brain activity eigenmodes previously derived from corticothalamic neural field theory. Just two eigenmodes that overlap in frequency are found to reproduce the observed topography of the classical alpha rhythm for subjects with a single, occipitally concentrated alpha peak in their electroencephalograms. Alpha frequency splitting and relative amplitudes of double alpha peaks are explored analytically and numerically within this four-mode framework using eigenfunction expansion and perturbation methods. These effects are found to result primarily from the different eigenvalues and corticothalamic gains corresponding to the eigenmodes. Three modes with two non-overlapping frequencies suffice to reproduce the observed topography for subjects with a double alpha peak, where the appearance of a distinct second alpha peak requires an increase of the corticothalamic gain of higher eigenmodes relative to the first. Conversely, alpha blocking is inferred to be linked to a relatively small attention-dependent reduction of the gain of the relevant eigenmodes, whose effect is enhanced by the near-critical state of the brain and whose sign is consistent with inferences from neural field theory. The topographies and blocking of the mu and tau rhythms within the alpha-band are explained analogously via eigenmodes. Moreover, the observation of three rhythms in the alpha band is due to there being exactly three members of the first family of spatially nonuniform modes. These results thus provide a simple, unified description of alpha band rhythms and enable experimental observations of spectral structure and topography to be linked directly to theory and underlying physiology. [ABSTRACT FROM AUTHOR]

Circadian rhythms are found in a wide range of organisms and have garnered significant research interest in the field of chronobiology. Under normal circadian function, metabolic regulation is temporally coordinated across tissues and behaviors within a 24 h period. Metabolites, as the closest molecular regulation to physiological phenotype, have dynamic patterns and their relationship with circadian regulation remains to be fully elucidated. In this study, untargeted brain metabolomics was employed to investigate the daily rhythms of metabolites at four time points corresponding to four typical physiological states in Vespertilio sinensis. Key brain metabolites and associated physiological processes active at different time points were detected, with 154 metabolites identified as rhythmic. Analyses of both metabolomics and transcriptomics revealed that several important physiological processes, including the pentose phosphate pathway and oxidative phosphorylation, play key roles in regulating rhythmic physiology, particularly in hunting and flying behaviors. This study represents the first exploration of daily metabolic dynamics in the bat brain, providing insights into the complex regulatory network of circadian rhythms in mammals at a metabolic level. These findings serve as a valuable reference for future studies on circadian rhythms in nocturnal mammals. [ABSTRACT FROM AUTHOR]

For the electroencephalogram- (EEG-) based motor imagery (MI) braincomputer interface (BCI) system, more attention has been paid to the advanced machine learning algorithms rather than the effective MI training protocols over past two decades. However, it is crucial to assist the subjects in modulating their active brains to fulfill the endogenous MI tasks during the calibration process, which will facilitate signal processing using various machine learning algorithms. Therefore, we propose a trial-feedback paradigm to improve MI training and introduce a non-feedback paradigm for comparison. Each paradigm corresponds to one session. Two paradigms are applied to the calibration runs of corresponding sessions. And their effectiveness is verified in the subsequent testing runs of respective sessions. Different from the non-feedback paradigm, the trial-feedback paradigm presents a topographic map and its qualitative evaluation in real time after each MI training trial, so the subjects can timely realize whether the current trial successfully induces the event-related desynchronization/event-related synchronization (ERD/ERS) phenomenon, and then they can adjust their brain rhythm in the next MI trial. Moreover, after each calibration run of the trial-feedback session, a feature distribution is visualized and quantified to show the subjects' abilities to distinguish different MI tasks and promote their self-modulation in the next calibration run. Additionally, if the subjects feel distracted during the training processes of the non-feedback and trial-feedback sessions, they can execute the blinking movement which will be captured by the electrooculogram (EOG) signals, and the corresponding MI training trial will be abandoned. Ten healthy participants sequentially performed the non-feedback and trial-feedback sessions on the different days. The experiment results showed that the trial-feedback session had better spatial filter visualization, more beneficiaries, higher average off-line and on-line classification accuracies than the non-feedback session, suggesting the trialfeedback paradigm's usefulness in subject's self-modulation and good ability to perform MI tasks. [ABSTRACT FROM AUTHOR]

The relation between electroencephalography (EEG) rhythms, brain functions, and behavioral correlates is well-established. Some physiological mechanisms underlying rhythm generation are understood, enabling the replication of brain rhythms in silico. This offers a pathway to explore connections between neural oscillations and specific neuronal circuits, potentially yielding fundamental insights into the functional properties of brain waves. Information theory frameworks, such as Integrated information Decomposition (Φ-ID), relate dynamical regimes with informational properties, providing deeper insights into neuronal dynamic functions. Here, we investigate wave emergence in an excitatory/inhibitory (E/I) balanced network of integrate and fire neurons with short-term synaptic plasticity. This model produces a diverse range of EEG-like rhythms, from low δ waves to high-frequency oscillations. Through Φ-ID, we analyze the network's information dynamics and its relation with different emergent rhythms, elucidating the system's suitability for functions such as robust information transfer, storage, and parallel operation. Furthermore, our study helps to identify regimes that may resemble pathological states due to poor informational properties and high randomness. We found, e.g., that in silicoβ and δ waves are associated with maximum information transfer in inhibitory and excitatory neuron populations, respectively, and that the coexistence of excitatory θ, α, and β waves is associated to information storage. Additionally, we observed that high-frequency oscillations can exhibit either high or poor informational properties, potentially shedding light on ongoing discussions regarding physiological versus pathological high-frequency oscillations. In summary, our study demonstrates that dynamical regimes with similar oscillations may exhibit vastly different information dynamics. Characterizing information dynamics within these regimes serves as a potent tool for gaining insights into the functions of complex neuronal networks. Finally, our findings suggest that the use of information dynamics in both model and experimental data analysis, could help discriminate between oscillations associated with cognitive functions and those linked to neuronal disorders. Author summary: Electroencephalography (EEG) records cortical brain activity and is widely used in neuroscience for identifying cognitive states and diagnosing brain pathologies. However, the relationship between functional brain states and specific rhythms is sometimes unclear. Traditional methods combined with computational models often fail to link dynamical regimes to their possible functions. To address this, we used a computational model that generates in silico EEG-like signals in a neuron population. Instead of only analyzing spectral features, we focused our study on information flow in the neuron population between small groups of inhibitory and excitatory neurons during the emergence of different rhythms. We found that in some regimes, the system exhibits enhanced computational properties, with excitatory neurons maintaining parallel processing capacities, inhibitory neurons showing high robustness, or populations maximizing information transfer. In other regimes, low information flow results in more random behavior. Our work highlights the utility of informational dynamic analysis for understanding the relationship between emerging neuronal waves and functions in in silico neuronal populations, a fact that stimulates extending the present study to neuronal cultures and in vivo EEG time series. [ABSTRACT FROM AUTHOR]

Background: Binge eating (BE) is associated with a range of cognitive control deficits related to impulsivity, including lower response inhibition, preference for immediate gratification, and maladaptive decision-making. The aim was to investigate whether impulsivity and BE may interact with the decision process and underlying brain activity in outpatients with overweight or obesity who are starting a treatment to achieve weight loss. Methods: A sample of 26 treatment-seeking outpatients with overweight or obesity was evaluated for impulsivity, BE, and temporal discounting rates. Impulsivity was measured with the Barratt Impulsiveness Scale (BIS-11), according to which two groups were composed: high BIS and low BIS; BE was assessed with the eating disorders module of the Structured Clinical Interview for DSM5—Research Version, according to which two groups were composed: with (BE group) or without BE (NBE group). Changes in subjective value of rewards were measured with the Temporal Discounting Task (TDt) where participants had to choice between sooner but smaller vs. later but larger monetary rewards. These choices were made in two differently delayed conditions ("Now" and "Not-now"). Brain rhythms were recorded through high-density electroencephalogram (hd-EEG) during the TDt. Results: Patients with BE reported more impulsive tendencies and perceived sooner rewards as more gratifying when both options were delayed (Not-now condition, p = 0.02). The reward choice in the TDt was accompanied by a general EEG alpha band desynchronization in parietal areas observed without differences between experimental conditions and patients groups. No effects were observed within the Now condition or in the other EEG bands. Conclusions: The tendency to favor immediate rewards may constitute an obstacle to adhering to treatment plans and achieving weight loss goals for outpatients with overweight or obesity. Clinicians are therefore encouraged to include psychological factors, such as impulsivity and dysfunctional eating behaviors, when designing weight loss programs. By addressing these psychological aspects, clinicians can better support patients in overcoming barriers to adherence and achieving sustainable weight loss. Trial registration: This study was approved by the Ethics Committee of the Department of Psychological, Health, and Territorial Sciences of the University G. d'Annunzio of Chieti-Pescara (Prot. n. 254 of 03/14/2017). Plain English Summary: Although the health risk of overweight and obesity is widely acknowledged, weight loss treatments are often unsuccessful in the long term due to several factors. Binge eating (BE) might explain some of the reduced benefits from the treatments. In this study, we investigated whether impulsivity and BE may interact decision processes among outpatients with overweight or obesity, and we looked at their underlying brain activity. Patients with BE tended to be more impulsive and to prefer sooner rewards compared to later ones. In all patients, α oscillations in the parietal lobe have been observed to play a dominant role during reward choice processes. A preference for immediate rewards might help to explain the low long-term success of weight loss treatment plans. The study may also provide useful information for clinicians working with patients with overweight or obesity. [ABSTRACT FROM AUTHOR]

FKBP51, also known as FK506‐binding protein 51, is a molecular chaperone and scaffolding protein with significant roles in regulating hormone signaling and responding to stress. Genetic variants in FKBP5, which encodes FKBP51, have been implicated in a growing number of neuropsychiatric disorders, which has spurred efforts to target FKBP51 therapeutically. However, the molecular mechanisms and sub‐anatomical regions influenced by FKBP51 in these disorders are not fully understood. In this study, we aimed to examine the impact of Fkbp5 ablation using circadian phenotyping and molecular analyses. Our findings revealed that the lack of FKBP51 did not significantly alter circadian rhythms, as detected by wheel‐running activity, but did offer protection against stress‐mediated disruptions in rhythmicity in a sex‐dependent manner. Protein changes in Fkbp5 KO mice, as measured by histology and proteomics, revealed alterations in a brain region‐ and sex‐dependent manner. Notably, regardless of sex, aged Fkbp5 KOs showed elevated MYCBP2, FBXO45, and SPRYD3 levels, which are associated with neuronal‐cell adhesion and synaptic integrity. Additionally, pathways such as serotonin receptor signaling and S100 family signaling were differentially regulated in Fkbp5 KO mice. Weighted protein correlation network analysis identified protein networks linked with synaptic transmission and neuroinflammation. The information generated by this work can be used to better understand the molecular changes in the brain during aging and in the absence of Fkbp5, which has implications for the continued development of FKBP51‐focused therapeutics for stress‐related disorders. [ABSTRACT FROM AUTHOR]

Introduction: Massage therapy reduces pain, alleviates muscle tension, and enhances psychological well-being. In addition to subjective methods for assessing changes in well-being due to massage, researchers are increasingly seeking effective objective methods. One such method is electroencephalogram (EEG) analysis. This study aimed to conduct a systematic review to evaluate the effects of different forms of massage on EEG recordings in healthy adult subjects. Materials and methods: To conduct the review, the databases PubMed, Medline, ScienceDirect, and Google Scholar were searched for publications from 2012 to 2023. The keywords used were: massage therapy, EEG, and electroencephalography. The available publications were screened against the established inclusion and exclusion criteria, resulting in 7 papers being included in the final analysis. Results: In the papers included in the analysis, the intervention included aromatherapy massage, mechanical massage, Swedish massage, and foot reflexology. Most of the papers found changes in brainwave amplitude under the influence of massage; however, the directions of change (decrease/increase in amplitude) varied within the range of individual waves. Conclusions: Based on the literature review, it can be concluded that massage affects EEG recordings in healthy adults. However, this effect appears to depend on the type of massage applied. The significant discrepancies in the interventions used indicate a need for further research and analysis involving standardized massage protocols. [ABSTRACT FROM AUTHOR]

A Brain-Computer Interface (BCI) is a system that allows a person to control external devices using only their brain activity. It works by translating brain signals into commands that can be understood by a computer. Several lines of evidence demonstrated the deleterious effect of methamphetamine (MA) on neurological and psychological functions. The use of amphetamines, such as MA, is associated with cerebrovascular complications such as cerebrovascular accidents (CVA), hemorrhage, hypoxic damage and vasculitis. Interestingly, while changes to cerebral blood flow (CBF) in response to acute amphetamine exposure have been reported. Transcranial Color Doppler (TCCD) is a non-invasive medical imaging technique that uses ultrasound waves to measure blood flow velocity in the major arteries of the brain, specifically within the circle of Willis. The research paper you referenced explores the use of TCCD as a potential measurement modality for BCIs. Quantitative electroencephalogram (qEEG) is a powerful tool for understanding brain function qEEG can reveal specific brain wave patterns associated with drug addiction, potentially providing insights into the neurobiological mechanisms underlying cravings, withdrawal symptoms, and relapse risk in Methamphetamine User Disorder (MUD). There is growing research interest in using Transcranial dopller as a measurement modality for BCIs.Here are some of the key considerations for using Transcranial doppler in BCIs: Mental Tasks, signal processing and classification, accuracy and reliability. Transcranial doppler provides information about blood flow in specific arteries but lacks detailed spatial information about brain activity. These patterns could vary depending on the type of drug, the severity of addiction, and individual differences. Transcranial doppler in measuring middle cerebral artery (MCA) blood flow velocity parameters (peak systolic velocity (PSV) and mean flow velocity (MFV)). qEEG can help researchers investigate the complex interplay between addiction and other brain disorders, like depression or anxiety. Characteristic qEEG in drugs addiction Increased Theta (4-8 Hz) and delta (1-4 Hz) brain waves are often associated with sleep and relaxation. However, research has shown that individuals with drug addiction may have increased theta and delta activity, particularly in the frontal and temporal regions of the brain. Altered Beta (13-30 Hz) brain waves are generally associated with wakefulness, alertness, and cognitive processing. Studies have observed both increases and decreases in beta activity in individuals with drug addiction, depending on the type of drug, the stage of addiction, and the specific brain regions being examined. The results of this research have important practical implications for building an diagnostic and functional assement with a better understanding of an using technology. [ABSTRACT FROM AUTHOR]

There exists a scarcity of neurocognitive research that has successfully used electrophysiology as an objective tool to assess different learning modalitiesthat are commonly used in anatomy. The objective of this study was to explore the trend in the expression of brain waves in medical students when using different didactic strategies in learning anatomy. An exploratory, observational, cross-sectional study was conducted on 22 firstsemester medical students in anatomy. Using didactic strategies with different pedagogical tools such as biological models (human skulls and pig hearts) and/or digital application (Visible Body software), and different evaluation methods, students were grouped into three groups: "EOR1" students who learned using a skull and were evaluated with it; "EOV1" students who learned using a pig heart and were evaluated with the Visible Body software; and "EOV2" for students who learned through the Visible Body software and were evaluated with it. During the evaluation, each student was monitored in real-time using an electroencephalogram and the Emotiv EPOC brain-computer interface system to record the electro-physiological activity of beta (B), theta (T), and gamma (G) waves. The data obtained (the trend of the electrical activity of each wave and its relationship with students' performance in each didactic strategy) were analyzed using Pearson's correlation coefficient. Groups EOR1 and EOV1 had satisfactory results in their evaluations and showed a predominance of B waves (x=67.33) and T waves (x=57.67). Additionally, a strongly positive correlation (r=0.82) was found between the presence of B waves and the duration of T waves. Students who used three-dimensional biological models as a pedagogical tool showed a predominance of B and T waves, which are associated with sensorimotor and exploratory learning necessary for studying anatomy. [ABSTRACT FROM AUTHOR]

Background: Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive neuromodulation technique that shows promise for the treatment of Parkinson's disease (PD). However, there is still limited understanding of the optimal stimulation frequencies and whether rTMS can alleviate PD symptoms by regulating the CaMKII-CREB-BMAL1 pathway. Methods: A PD mouse model was induced intraperitoneally with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and treated with 1 Hz, 5 Hz, and 10 Hz rTMS. The neurological function, survival of dopaminergic neurons, and protein levels of Tyrosine hydroxylase (TH), α-synuclein(α-syn), and brain-derived neurotrophic factor (BDNF) in the striatum were measured to determine the optimal stimulation frequencies of rTMS treatment in PD mice. The levels of melatonin, cortisol, and the circadian rhythm of Brain and muscle ARNT-like 1 (BMAL1) in PD model mice were detected after optimal frequency rTMS treatment. Additionally, KN-93 and Bmal1siRNA interventions were used to verify that rTMS could alleviate PD symptoms by regulating the CaMKII-CREB-BMAL1 pathway. Results: Administration of 10 Hz rTMS significantly improved neurological function, increased the protein levels of TH and BDNF, and inhibited abnormal aggregation of a-syn. Furthermore, administration of 10 Hz rTMS regulated the secretion profile of cortisol and melatonin and reversed the circadian arrhythmia of BMAL1 expression. After the KN-93 intervention, the MPTP+rTMS+KN-93 group exhibited decreased levels of P- Ca2+/calmodulin-dependent protein kinase II (CaMKII)/CaMKII, P-cAMP-response-element-binding protein (CREB)/CREB, BMALI, and TH. After Bmal1siRNA intervention, the protein levels of BMAL1 and TH were significantly reduced in the MPTP+10 Hz+ Bmal1siRNA group. At the same time, there were no significant changes in the proportions of P-CaMKIIα/CaMKIIα and P-CREB/CREB expression levels. Finally, immunohistochemical analysis showed that the number of TH-positive neurons was high in the MPTP+10 Hz group, but decreased significantly after KN-93 and Bmal1siRNA interventions. Conclusion: Treatment with 10 Hz rTMS alleviated MPTP-induced PD symptoms by regulating the CaMKII-CREB-BMAL1 pathway. This study provides a comprehensive perspective of the therapeutic mechanisms of rTMS in PD. [ABSTRACT FROM AUTHOR]

Multivariate entropy algorithms have proven effective in the complexity dynamic analysis of electroencephalography (EEG) signals, with researchers commonly configuring the variables as multi-channel time series. However, the complex quantification of brain dynamics from a multi-frequency perspective has not been extensively explored, despite existing evidence suggesting interactions among brain rhythms at different frequencies. In this study, we proposed a novel algorithm, termed multi-frequency entropy (mFreEn), enhancing the capabilities of existing multivariate entropy algorithms and facilitating the complexity study of interactions among brain rhythms of different frequency bands. Firstly, utilizing simulated data, we evaluated the mFreEn's sensitivity to various noise signals, frequencies, and amplitudes, investigated the effects of parameters such as the embedding dimension and data length, and analyzed its anti-noise performance. The results indicated that mFreEn demonstrated enhanced sensitivity and reduced parameter dependence compared to traditional multivariate entropy algorithms. Subsequently, the mFreEn algorithm was applied to the analysis of real EEG data. We found that mFreEn exhibited a good diagnostic performance in analyzing resting-state EEG data from various brain disorders. Furthermore, mFreEn showed a good classification performance for EEG activity induced by diverse task stimuli. Consequently, mFreEn provides another important perspective to quantify complex dynamics. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) is a physiological signal directly generated by the central nervous system. Brain rhythm is closely related to a person's emotional state and is widely used for EEG emotion recognition. In previous studies, the rhythm specificity between different brain channels was seldom explored. In this paper, the rhythm specificity of brain channels is studied to improve the accuracy of EEG emotion recognition. Variational mode decomposition is used to decompose rhythm signals and enhance features, and two kinds of information entropy, i.e., differential entropy (DE) and dispersion entropy (DispEn) are extracted. The rhythm being used to get the best result of single channel emotion recognition is selected as the representative rhythm, and the remove one method is employed to obtain rhythm information entropy feature. In the experiment, the DEAP database was used for EEG emotion recognition in valence-arousal space. The results showed that the best result of rhythm DE feature classification in the valence dimension is 77.04%, and the best result of rhythm DispEn feature classification in the arousal dimension is 79.25%. [ABSTRACT FROM AUTHOR]