Beta Rhythms Research Articles

Optimal segmentation of non-linear and non-stationary time series based on fractal dimension and Poincare section and its application in solving EEG-signal

Non-linearity and non-stationarity characteristics in physiological and biological time series limit the reliability of conventional analysis. The main challenge in pattern recognition problems is determining the Optimal-Window-Duration (OWD) for segmenting these time series. Our study presents an innovative algorithm, based on Individual-Optimal-Segmentation (IOS), which integrates chaos theory using fractal dimension, and Poincare section into the estimation of the OWD for segmenting time series with non-linear and non-stationary characteristics. In the logistic map case study, the IOS algorithm proved to be effective in detecting cycles in a range of numerical examples, from period-2 to chaotic behavior. So that its performance was superior to previous approaches, especially when it comes to detecting chaotic behaviors. Furthermore, the combination of the IOS algorithm with the Welch signal processing method shows promise in identifying abnormal Power-Spectral-Density (PSD) curves related to Beta and Gamma rhythms in depression diagnosis scenarios. The generated depression diagnosis system attained an accuracy (%) of 99.07±0.35 (with 2-channel) using the Monte Carlo simulation method. These findings suggest a potentially valuable tool for addressing segmentation challenges in time series analysis, particularly in contexts involving complex, dynamic data. While, it is suggested that future research investigate the computational efficiency and robustness of the IOS algorithm when handling large-scale or real-time data and increase its application in various fields in data science and signal processing. Furthermore, expanding the application range of the IOS algorithm for other pattern recognition problems with physiological or biological data can strengthen the generalizability and practical application of the algorithm.

Neurofeedback Training in Children with ADHD: A Systematic Review of Personalization and Methodological Features Facilitating Training Conditions.

Objective. Current research on the effectiveness of neurofeedback (NFB) in children with attention-deficit/hyperactivity disorder (ADHD) is divided. Personalized NFB (pNFB), using pre-recorded individual electroencephalogram (EEG) features, is hypothesized to provide more reliable results. Our paper reviews available evidence on pNFB effectiveness and its methodological quality. Additionally, it explores whether other methodological features implying personalization are related to successful NFB. Methods. We conducted a systematic literature review on PubMed, PSYNDEX, PsycInfo and PsycArticles until November, 30, 2023. Studies that focused on pNFB in children with ADHD were selected, deviant studies excluded. Quality ratings by independent raters using Loney's1 criteria were conducted. Pooled effect sizes for NFB effects and methodological features were calculated. Results. Three of 109 studies included personalization and were reviewed in the full-text. In two studies, theta/beta-NFB was personalized using individual alpha peak frequencies (iAPF), whereas in one study, individual beta rhythms were trained. All three studies demonstrated significant short- and long-term improvements in ADHD symptoms, as assessed by questionnaires and objective performance tests, when compared to standard protocols (SP), sham-NFB, and control conditions. Twelve of 111 studies reported methodological features consistently related to NFB effectiveness. These features, including self-control instructions, feedback animations, timing of feedback presentation, behavioral performance, pre-recorded individual ERP-components and stimulant medication dosage, can be used to personalize NFB and enhance training success. Conclusion. Personalizing NFB with iAPF appears promising based on the existing -albeit small- body of research. Future NFB studies should include iAPF and other personalized features facilitating implementation consistently associated with treatment success.

Emotion Classification System for ASD Group by Using Wireless EEG Monitoring Device

This paper delves into a study employing dry electroencephalograms (EEG) as input features to discern emotions in individuals with autism spectrum disorders (ASD). While EEG is a prevalent tool in emotion classification studies for typically developing individuals, less attention has been directed towards its application in the ASD population. In this study, ten participants diagnosed with ASD wore wireless dry EEG sensors to capture their EEG signals. These signals encompassed alpha, beta, delta, theta, and gamma waves, which were subsequently subjected to feature extraction techniques such as the t-test, principal component analysis (PCA), ReliefF, and Chi-Square. Classification of positive, neutral, and negative emotions was performed using various algorithms, including K-nearest neighbor (KNN), Multinomial Logistic Regression (MLR), Naive Bayes (NB), Random Tree (RT), Random Forest (RF), and Support Vector Machine (SVM). Ultimately, employing SVM with a t-test enhanced the accuracy of emotion classification for the ASD group from 66.4% to 74.1%.

Exploring gender differences in acoustic-thermal comfort and performance in a simulated working environment

This study investigated gender differences in acoustic-thermal comfort and cognitive performance in three workload levels in eight simulated work environments with two noise levels (55 and 75 dBA) and four air temperatures (18, 22, 26, and 30 °C). Subjective perceptions, including noise annoyance and fatigue, physiological measurements like Heart Rate Variability (HRV), respiration rate, and EEG variables (ALPHA, BETA, THETA) were used to assess comfort. In addition, the cognitive performance of men and women was evaluated on three levels: simple, medium, and complex. Women generally reported higher annoyance levels than men, especially at 75 dB noise level and increasing temperatures. Women experienced higher fatigue in warmer conditions than men. Women exhibited higher accuracy and lower reaction time than men at 18 °C and 22 °C with a quiet noise level (55 dBA) and simple and medium tasks. However, women acted accurately in complex tasks but had a higher reaction time. On the contrary, as the tasks are complicated and air temperature (26 °C and 30 °C) increases, men have higher accuracy and further reaction time than women. Accuracy and reaction time were diminished in both genders at 75 dBA noise level. Significant changes in the physiological parameters were observed at 18–22 °C for men and at 26–30 °C for women with the same noise level. Women had higher alpha and beta waves at 18–22 °C, indicating greater comfort and alertness, but these values decreased as air temperature rose. Overall, the study emphasizes the importance of considering gender differences when designing indoor work environments for optimal comfort and performance.

Comparing the effects of resistance and aerobic exercise on mood-related symptoms and EEG activity in young healthy adults: A non-randomized pilot study

Background and aimsExercise offers several benefits in combating mood-related symptoms in humans. Participation in various types of physical activity improves brain and mental health. However, the different effects and mechanisms depending on the type of exercise on an individual's mood and brain remain unclear. This pilot trial aimed to reveal the feasibility of the effects of resistance and aerobic exercise on mood-related symptoms and explain the different patterns of brain waves. MethodsThis 15-week single-arm pre-test/post-test trial including 3 measurement periods was held in Seoul, Republic of Korea. Overall, 18 healthy participants aged 19–29 years were recruited. After 6 weeks of respective aerobic and resistance exercise, anxiety/depression-related questionnaire scores and electroencephalography (EEG) changes were measured (single blinded outcome assessment). Specifically, changes in frontal alpha asymmetry (FAA), absolute band power of beta waves, and theta waves were measured in the prefrontal and frontal regions, which play a critical role in regulating mood and cognition. ResultsResistance exercise resulted in a significant reduction in Beck Depression Inventory-II scores (p = 0.002, effect size = 0.76) while significant improvement was shown in Beck Anxiety Inventory scores (p = 0.01, effect size = 0.77) after aerobic exercise. In regarding EEG analysis, ten participants completed the assessment. After resistance exercise, there was a significant elevation in frontal beta waves (p = 0.01, effect size = 0.81). Aerobic exercise did not result in significant EEG changes. There were no adverse events related to study participation. ConclusionOur findings suggest that resistance and aerobic exercises may be feasible for the alleviation of mood-related symptoms and EEG activities. As our pilot trial has a small sample size and did not include a control group, future research that accurately reflects the effects of different types of exercises on mood symptoms and changes of EEG activities is required.

From Infancy to Childhood: A Comprehensive Review of Event- and Task-Related Brain Oscillations.

Brain development from infancy through childhood involves complex structural and functional changes influenced by both internal and external factors. This review provides a comprehensive analysis of event and task-related brain oscillations, focusing on developmental changes across different frequency bands, including delta, theta, alpha, beta, and gamma. Electroencephalography (EEG) studies highlight that these oscillations serve as functional building blocks for sensory and cognitive processes, with significant variations observed across different developmental stages. Delta oscillations, primarily associated with deep sleep and early cognitive demands, gradually diminish as children age. Theta rhythms, crucial for attention and memory, display a distinct pattern in early childhood, evolving with cognitive maturation. Alpha oscillations, reflecting thalamocortical interactions and cognitive performance, increase in complexity with age. Beta rhythms, linked to active thinking and problem-solving, show developmental differences in motor and cognitive tasks. Gamma oscillations, associated with higher cognitive functions, exhibit notable changes in response to sensory stimuli and cognitive tasks. This review underscores the importance of understanding oscillatory dynamics to elucidate brain development and its implications for sensory and cognitive processing in childhood. The findings provide a foundation for future research on developmental neuroscience and potential clinical applications.

Effects of Exercise Training on EEG Activity Patterns during Cognitive Tasks

To explore the effective method to detect the effect of exercise training on EEG activity, this paper compared the distinguishability of EEG changes relevanted to exercise training in different cognitive states. EEG signals of college students who have undergone long-term exercise training (exercise training group) and college students who have not undergone such training (control group) were collected under specific cognitive tasks (Motor imagination tasks). Then the power characteristics of alpha wave and beta wave were obtained by wavelet transform method. The machine learning method was used to distinguish the difference of brain electrical activity between the exercise-trained subjects and the control group during the exercise-related cognitive tasks. Results showed that EEG patterns between two groups were not distinguished in rest status (the accuracy is lower than 59%), but they were separable in the completion of the motor cognitive tasks. The accuracy was over 85%. The pattern of brain electrical activity during cognitive tasks can better reflect the effect of exercise training.

Imaging high-frequency voltage dynamics in multiple neuron classes of behaving mammals.

Fluorescent genetically encoded voltage indicators report transmembrane potentials of targeted cell-types. However, voltage-imaging instrumentation has lacked the sensitivity to track spontaneous or evoked high-frequency voltage oscillations in neural populations. Here we describe two complementary TEMPO voltage-sensing technologies that capture neural oscillations up to ~100 Hz. Fiber-optic TEMPO achieves ~10-fold greater sensitivity than prior photometry systems, allows hour-long recordings, and monitors two neuron-classes per fiber-optic probe in freely moving mice. With it, we uncovered cross-frequency-coupled theta- and gamma-range oscillations and characterized excitatory-inhibitory neural dynamics during hippocampal ripples and visual cortical processing. The TEMPO mesoscope images voltage activity in two cell-classes across a ~8-mm-wide field-of-view in head-fixed animals. In awake mice, it revealed sensory-evoked excitatory-inhibitory neural interactions and traveling gamma and 3-7 Hz waves in the visual cortex, and previously unreported propagation directions for hippocampal theta and beta waves. These technologies have widespread applications probing diverse oscillations and neuron-type interactions in healthy and diseased brains.

Detection of Pilots' Psychological Workload during Turning Phases Using EEG Characteristics.

Pilot behavior is crucial for aviation safety. This study aims to investigate the EEG characteristics of pilots, refine training assessment methodologies, and bolster flight safety measures. The collected EEG signals underwent initial preprocessing. The EEG characteristic analysis was performed during left and right turns, involving the calculation of the energy ratio of beta waves and Shannon entropy. The psychological workload of pilots during different flight phases was quantified as well. Based on the EEG characteristics, the pilots' psychological workload was classified through the use of a support vector machine (SVM). The study results showed significant changes in the energy ratio of beta waves and Shannon entropy during left and right turns compared to the cruising phase. Additionally, the pilots' psychological workload was found to have increased during these turning phases. Using support vector machines to detect the pilots' psychological workload, the classification accuracy for the training set was 98.92%, while for the test set, it was 93.67%. This research holds significant importance in understanding pilots' psychological workload.

Effect of Infra Low Frequency (ILF) neurofeedback training on EEG in children with autism spectrum disorders.

To investigate whether Infra-low frequency Neurofeedback (ILF-NFB) training can improve brain electrical activity in children with autism spectrum disorders ASD. This single arm pre and post intervention study was carried out at IBMS (Institute of Basic Medical Sciences), Khyber Medical University, Peshawar and Shaheed Zulfiqar Ali Bhutto Medical University (SZABMU), Islamabad from January 2021 to December 2022. A purposive sampling technique was used. Thirty-five ASD children (male=24; female=11; 7-17 years) were provided with 30 sessions of infra low frequency (ILF) neurofeedback training for 15-20 minutes, during 10 weeks. Childhood Autism Rating Scale (CARS) scoring was done and electroencephalogram (EEG) activity was compared before and after ILF-NF training sessions. Around 62.9% participants had mild-moderate autism and 37.1% had severe autism. Wilcoxon Signed rank test revealed a significant decline in delta (Pre-test=47.31±19.22, Post-test=22.07±6.83; p=<0.001), theta (Pre-test=24.75±16.62, Post-test=12.37±3.59; p=<0.001) and alpha (Pre-test=12.01±9.81, Post-test=4.03±1.61; p=< 0.001) waves. Mann Whitney U test exhibited no significant gender differences in EEG pattern before and after neurofeedback except in theta waves (p=0.03) before the intervention. Decline in delta, theta, beta and alpha waves propose that ILF-NF training can be effective in improving the EEG activity. ILF-NFB can be perceived as a valuable non-invasive, non-pharmacological intervention for improving EEG pattern via reintegration of brain activity resulting in increased the attention and focus, enhanced mental stability and cognitive engagement.

Effects of acute administration of 4-allyl-2,6-dimethoxyphenol in mouse models of seizures

Epilepsy, a chronic neurological disorder characterized by recurrent unprovoked seizures, presents a substantial challenge in approximately one-third of cases exhibiting resistance to conventional pharmacological treatments. This study investigated the effect of 4-allyl-2,6-dimethoxyphenol, a phenolic compound derived from various natural sources, in different models of induced seizures and its impact on animal electroencephalographic (EEG) recordings. Adult male Swiss albino mice were pre-treated (i.p.) with a dose curve of 4-allyl-2,6-dimethoxyphenol (50, 100, or 200 mg/kg), its vehicle (Tween), or standard antiepileptic drug (Diazepam; or Phenytoin). Subsequently, the mice were subjected to different seizure-inducing models – pentylenetetrazole (PTZ), 3-mercaptopropionic acid (3-MPA), pilocarpine (PILO), or maximal electroshock seizure (MES). EEG analysis was performed on other animals surgically implanted with electrodes to evaluate brain activity. Significant results revealed that animals treated with 4-allyl-2,6-dimethoxyphenol exhibited increased latency to the first myoclonic jerk in the PTZ and PILO models; prolonged latency to the first tonic-clonic seizure in the PTZ, 3-MPA, and PILO models; reduced total duration of tonic-clonic seizures in the PTZ and PILO models; decreased intensity of convulsive seizures in the PTZ and 3-MPA models; and diminished mortality in the 3-MPA, PILO, and MES models. EEG analysis indicated an increase in the percentage of total power attributed to beta waves following 4-allyl-2,6-dimethoxyphenol administration. Notably, the substance protected from behavioral and electrographic seizures in the PTZ model, preventing increases in the average amplitude of recording signals while also inducing an increase in the participation of theta and gamma waves. These findings suggest promising outcomes for the tested phenolic compound across diverse pre-clinical seizure models, highlighting the need for further comprehensive studies to elucidate its underlying mechanisms and validate its clinical relevance in epilepsy management.

Return of intracranial beta oscillations and traveling waves with recovery from traumatic brain injury.

Traumatic brain injury (TBI) remains a pervasive clinical problem associated with significant morbidity and mortality. However, TBI remains clinically and biophysically ill-defined, and prognosis remains difficult even with the standardization of clinical guidelines and advent of multimodality monitoring. Here we leverage a unique data set from TBI patients implanted with either intracranial strip electrodes during craniotomy or quad-lumen intracranial bolts with depth electrodes as part of routine clinical practice. By extracting spectral profiles of this data, we found that the presence of narrow-band oscillatory activity in the beta band (12-30 Hz) closely corresponds with the neurological exam as quantified with the standard Glasgow Coma Scale (GCS). Further, beta oscillations were distributed over the cortical surface as traveling waves, and the evolution of these waves corresponded to recovery from coma, consistent with the putative role of waves in perception and cognitive activity. We consequently propose that beta oscillations and traveling waves are potential biomarkers of recovery from TBI. In a broader sense, our findings suggest that emergence from coma results from recovery of thalamo-cortical interactions that coordinate cortical beta rhythms.

Различия в спектральной мощности ЭЭГ и поведении детей с аутистическими расстройствами несиндромального происхождения и с синдромом Дауна

&lt;p style="text-align: justify;"&gt;The diverse nature of the autistic spectrum disorders group (ASD) may be associated with EEG patterns, which may be specific for different profiles of ASD. Approximately 15% of children with Down syndrome (DS) exhibit autistic features, and the cognitive impairment associated with this syndrome remains unclear. We aimed to explore the potential similarities in behavioral disorders and their EEG correlates between non-syndromic ASD and DS children with comorbid autistic features. Three groups of right-handed boys participated in the experiments: typically developed boys (&amp;ldquo;control&amp;rdquo;) &amp;mdash; N = 36; mean age = 7.11, SD = 0.86; DS group age N = 15, mean age 7.2 (SD = 0.94), and ASD group; N &amp;mdash; 36; mean age &amp;mdash; 7.11, SD &amp;mdash; 0.86; DS group age N &amp;mdash; 15, mean age 7.2 (SD &amp;mdash; 0.94), and ASD group; N &amp;mdash; 39; mean age &amp;mdash; 6.24 (SD &amp;mdash; 0.91). Increased theta rhythm spectral power in EEG was found in the frontal and temporal areas, while the alpha rhythm in the occipital area exhibited a reduction in children with DS and ASD as compared with control. The alterations in bioelectric activity, such as increased spectral power f theta in the frontal area and a reduction of EEG spectral power in the occipital alpha band, were partially similar in children with DS and with ASD, albeit less pronounced in DS. A distinctive EEG characteristic of DS was the heightened spectral power of beta rhythm compared to the control group. The Psychoeducational Profile testing in DS revealed inferior results in verbal development (p &amp;lt; 0.05) and cognitive representations (p &amp;lt; 0.05) compared to both the control and ASD groups. However, no differences were found in the total cognitive development scale between DS and ASD. At the behavioral level, impaired verbal and non-verbal intelligence was more prominent in DS than in ASD.&lt;/p&gt;

Neurochemical organization of cortical proteinopathy and neurophysiology along the Alzheimer's disease continuum.

Despite parallel research indicating amyloid-β accumulation, alterations in cortical neurophysiological signaling, and multi-system neurotransmitter disruptions in Alzheimer's disease (AD), the relationships between these phenomena remains unclear. Using magnetoencephalography, positron emission tomography, and an atlas of 19 neurotransmitters, we studied the alignment between neurophysiological alterations, amyloid-β deposition, and the neurochemical gradients of the cortex. In patients with mild cognitive impairment and AD, changes in cortical rhythms were topographically aligned with cholinergic, serotonergic, and dopaminergic systems. These alignments correlated with the severity of clinical impairments. Additionally, cortical amyloid-β plaques were preferentially deposited along neurochemical boundaries, influencing how neurophysiological alterations align with muscarinic acetylcholine receptors. Most of the amyloid-β-neurochemical and alpha-band neuro-physio-chemical alignments replicated in an independent dataset of individuals with asymptomatic amyloid-β accumulation. Our findings demonstrate that AD pathology aligns topographically with the cortical distribution of chemical neuromodulator systems and scales with clinical severity, with implications for potential pharmacotherapeutic pathways. Changes in cortical rhythms in Alzheimer's are organized along neurochemical boundaries. The strength of these alignments is related to clinical symptom severity. Deposition of amyloid-β (Aβ) is aligned with similar neurotransmitter systems. Aβ deposition mediates the alignment of beta rhythms with cholinergic systems. Most alignments replicate in participants with pre-clinical Alzheimer's pathology.

How to design optimal brain stimulation to modulate phase-amplitude coupling?

Objective. Phase-amplitude coupling (PAC), the coupling of the amplitude of a faster brain rhythm to the phase of a slower brain rhythm, plays a significant role in brain activity and has been implicated in various neurological disorders. For example, in Parkinson’s disease, PAC between the beta (13–30 Hz) and gamma (30–100 Hz) rhythms in the motor cortex is exaggerated, while in Alzheimer’s disease, PAC between the theta (4–8 Hz) and gamma rhythms is diminished. Modulating PAC (i.e. reducing or enhancing PAC) using brain stimulation could therefore open new therapeutic avenues. However, while it has been previously reported that phase-locked stimulation can increase PAC, it is unclear what the optimal stimulation strategy to modulate PAC might be. Here, we provide a theoretical framework to narrow down the experimental optimisation of stimulation aimed at modulating PAC, which would otherwise rely on trial and error. Approach. We make analytical predictions using a Stuart–Landau model, and confirm these predictions in a more realistic model of coupled neural populations. Main results. Our framework specifies the critical Fourier coefficients of the stimulation waveform which should be tuned to optimally modulate PAC. Depending on the characteristics of the amplitude response curve of the fast population, these components may include the slow frequency, the fast frequency, combinations of these, as well as their harmonics. We also show that the optimal balance of energy between these Fourier components depends on the relative strength of the endogenous slow and fast rhythms, and that the alignment of fast components with the fast rhythm should change throughout the slow cycle. Furthermore, we identify the conditions requiring to phase-lock stimulation to the fast and/or slow rhythms. Significance. Together, our theoretical framework lays the foundation for guiding the development of innovative and more effective brain stimulation aimed at modulating PAC for therapeutic benefit.

Beta-frequency sensory stimulation enhances gait rhythmicity through strengthened coupling between striatal networks and stepping movement.

Stepping movement is delta (1-4 Hz) rhythmic and depends on sensory inputs. In addition to delta rhythms, beta (10-30 Hz) frequency dynamics are also prominent in the motor circuits and are coupled to neuronal delta rhythms both at the network and the cellular levels. Since beta rhythms are broadly supported by cortical and subcortical sensorimotor circuits, we explore how beta-frequency sensory stimulation influences delta-rhythmic stepping movement, and dorsal striatal circuit regulation of stepping. We delivered audiovisual stimulation at 10 Hz or 145 Hz to mice voluntarily locomoting, while simultaneously recording stepping movement, striatal cellular calcium dynamics and local field potentials (LFPs). We found that 10 Hz, but not 145 Hz stimulation prominently entrained striatal LFPs. Even though sensory stimulation at both frequencies promoted locomotion and desynchronized striatal network, only 10 Hz stimulation enhanced the delta rhythmicity of stepping movement and strengthened the coupling between stepping and striatal LFP delta and beta oscillations. These results demonstrate that higher frequency sensory stimulation can modulate lower frequency dorsal striatal neural dynamics and improve stepping rhythmicity, highlighting the translational potential of non-invasive beta-frequency sensory stimulation for improving gait.

Rehabilitation Based on BCI: An Innovative Enhancement for Sensorimotor Cortex Rhythms Systemization.

The research proposes a novel strategy for categorizing electroencephalograms (EEG) in real-time brain-computer interfaces that have rehabilitation applications. The methodology utilizes Five Cross-Common Spatial Patterns (FCCSP) to develop a motor movement/imagery systemization model that extracts multi-domain characteristics with excellent performance. The goal is to eliminate the impact caused by EEG's nonstationarity. The article highlights the findings of a real-time technique that is incorporated into a comprehensive prediction system, and it offers an innovative method to boost accuracy in real-time Sensory-Motor cortex Rhythms (SMR). The accuracy increased from 57.14% using raw EEG to 85.71% after preprocessing, and from 58.08% to 97.94% in public domain SMR. The proposed Butterworth bandpass filter is optimized using the FCCSP to determine the ideal bandwidth that incorporates the whole EEG features in beta waves. The Hybrid Systemization of the Correlated Feature Removal classifier is then integrated with the FCCSP method to create improved predictive models. As a consequence, while applied to real-time and PhysioNet datasets, the outcome system achieved outstanding accuracy values of 85.71% and 97.94%, respectively. This demonstrates the robustness of the strategy to increase SMR prediction efficiency.

Cholinergic and behavior-dependent beta and gamma waves are coupled between olfactory bulb and hippocampus.

Olfactory oscillations may enhance cognitive processing through coupling with beta (β, 15-30 Hz) and gamma (γ, 30-160 Hz) activity in the hippocampus (HPC). We hypothesize that coupling between olfactory bulb (OB) and HPC oscillations is increased by cholinergic activation in control rats and is reduced in kainic-acid-treated epileptic rats, a model of temporal lobe epilepsy. OB γ2 (63-100 Hz) power was higher during walking and immobility-awake (IMM) compared to sleep, while γ1 (30-57 Hz) power was higher during grooming than other behavioral states. Muscarinic cholinergic agonist pilocarpine (25 mg/kg ip) with peripheral muscarinic blockade increased OB power and OB-HPC coherence at β and γ1 frequency bands. A similar effect was found after physostigmine (0.5 mg/kg ip) but not scopolamine (10 mg/kg ip). Pilocarpine increased bicoherence and cross-frequency coherence (CFC) between OB slow waves (SW, 1-5 Hz) and hippocampal β, γ1 and γ2 waves, with stronger coherence at CA1 alveus and CA3c than CA1 stratum radiatum. Bicoherence further revealed a nonlinear interaction of β waves in OB with β waves at the CA1-alveus. Beta and γ1 waves in OB or HPC were segregated at one phase of the OB-SW, opposite to the phase of γ2 and γ3 (100-160 Hz) waves, suggesting independent temporal processing of β/γ1 versus γ2/γ3 waves. At CA1 radiatum, kainic-acid-treated epileptic rats compared to control rats showed decreased theta power, theta-β and theta-γ2 CFC during baseline walking, decreased CFC of HPC SW with γ2 and γ3 waves during baseline IMM, and decreased coupling of OB SW with β and γ2 waves at CA1 alveus after pilocarpine. It is concluded that β and γ waves in the OB and HPC are modulated by a slow respiratory rhythm, in a cholinergic and behavior-dependent manner, and OB-HPC functional connectivity at β and γ frequencies may enhance cognitive functions.

Electrophysiological Markers Predicting Antipsychotic Treatment Response in Patients with Schizophrenia: A Retrospective Study.

This study aimed to provide an objective means of predicting treatment responses in patients with schizophrenia using quantitative electroencephalography (qEEG) as an electrophysiological indicator. We obtained qEEG recordings from patients with schizophrenia and explored them for patterns indicative of treatment responsiveness. The study included 68 patients had been diagnosed with schizophrenia spectrum disorder. After retrospectively gathering demographic information, clinical data such as qEEG, Positive and Negative Syndrome Scale (PANSS), a multiple regression analysis was performed. This analysis employed baseline qEEG findings as independent variables and PANSS score changes as dependent variables to discern causal relationships. The mean age of the participants was 38.4 years(SD =13.73). The mean PANSS score on admission was 92.97, decreasing to 67.41 at discharge. Multiple regression analysis revealed that delta waves in T4 (β=0.346, t=3.165, p=0.002), and high-beta waves in Fp2 (β=0.231, t=2.361, p=0.021) were associated with PANSS changes in absolute power. In addition, the delta waves of O2 (β=0.250, t=3.288, p=0.002); beta waves of T3 (β=-1.463, t=-5.423, p<0.001) and O2 (β=0.551, t=3.366, p=0.001); high beta waves of Fp1 (β=0.307, t=4.026, p<0.001), T3 (β=0.855, t=4.414, p<0.001) and T6 (β=-0.838, t=-4.559, p<0.001) of absolute power using the Z-score were also related to PANSS changes. Pearson's correlation analysis showed that only delta waves at Cz (r= 0.246, p=0.043) in absolute power correlated with changes in the PANSS. We found that certain qEEG wave patterns in patients with schizophrenia prior to antipsychotic treatment were linked to PANSS changes before and after treatment. Delta waves and beta waves, primarily in the frontal and temporal regions, were found to be significantly associated with changes in PANSS scores. In the future, the qEEG indicators identified in this study could serve as electrophysiological markers for predicting antipsychotic treatment responses in patients with schizophrenia.

Low and high beta rhythms have different motor cortical sources and distinct roles in movement control and spatiotemporal attention.

Low and high beta frequency rhythms were observed in the motor cortex, but their respective sources and behavioral correlates remain unknown. We studied local field potentials (LFPs) during pre-cued reaching behavior in macaques. They contained a low beta band (<20 Hz) dominant in primary motor cortex and a high beta band (>20 Hz) dominant in dorsal premotor cortex (PMd). Low beta correlated positively with reaction time (RT) from visual cue onset and negatively with uninstructed hand postural micro-movements throughout the trial. High beta reflected temporal task prediction, with selective modulations before and during cues, which were enhanced in moments of increased focal attention when the gaze was on the work area. This double-dissociation in sources and behavioral correlates of motor cortical low and high beta, with respect to both task-instructed and spontaneous behavior, reconciles the largely disparate roles proposed for the beta rhythm, by suggesting band-specific roles in both movement control and spatiotemporal attention.