Delta Band Power Research Articles

Relationship Between High Frequency Component of Heart Rate Variability and Delta EEG Power During Sleep in Women With Irritable Bowel Syndrome Compared to Healthy Women.

To explore the relationship between the high frequency (HF) heart rate variability (HRV) and electroencephalogram (EEG) delta band power in women with irritable bowel syndrome (IBS) versus healthy control women. Twenty women with IBS and twenty healthy controls were studied over three consecutive nights using polysomnography in a sleep laboratory. To avoid the first night effect, only second-night data were analyzed. Power spectral analysis was applied to HRV and EEG recordings. The linear system coherence/phase analysis assessed the relationship between normalized HF power of HRV and normalized delta band power of EEG during the first four NREM-REM sleep cycles. Women with IBS exhibited a significantly higher percentage of NREM sleep, higher normalized HF, lower normalized low frequency (LF) and decreased LF/HF ratio of HRV in the first four NREM-REM sleep cycles compared to controls. Additionally, their normalized delta band power was significantly lower in these sleep cycles and over the whole night. The phase shift between HF and delta band power was significantly longer in the IBS group. While the coherence between normalized HF and normalized delta band power was lower in the IBS group, the difference was not statistically significant. The coherence/phase analysis showed a dysregulated interaction between autonomic and central nervous systems in women with IBS, manifested by increased lag time between cardiac and EEG delta band power compared to healthy controls. Whether this dysregulation contributes to the pathophysiology of IBS remains to be determined.

Altered Resting-State Electroencephalogram Microstate Characteristics in Stroke Patients.

Stroke remains a leading cause of disability globally and movement impairment is the most common complication in stroke patients. Resting-state electroencephalography (EEG) microstate analysis is a non-invasive approach of whole-brain imaging based on the spatiotemporal pattern of the entire cerebral cortex. The present study aims to investigate microstate alterations in stroke patients. Resting-state EEG data collected from 24 stroke patients and 19 healthy controls matched by age and gender were subjected to microstate analysis. For four classic microstates labeled as class A, B, C and D, their temporal characteristics (duration, occurrence and coverage) and transition probabilities (TP) were extracted and compared between the two groups. Furthermore, we explored their correlations with clinical outcomes including the Fugl-Meyer assessment (FMA) and the action research arm test (ARAT) scores in stroke patients. Finally, we analyzed the relationship between the temporal characteristics and spectral power in frequency bands. False discovery rate (FDR) method was applied for correction of multiple comparisons. Microstate analysis revealed that the stroke group had lower occurrence of microstate A which was regarded as the sensorimotor network (SMN) compared with the control group (p = 0.003, adjusted p = 0.036, t = -2.959). The TP from microstate A to microstate D had a significant positive correlation with the Fugl-Meyer assessment of lower extremity (FMA-LE) scores (p = 0.049, r = 0.406), but this finding did not survive FDR adjustment (adjusted p = 0.432). Additionally, the occurrence and the coverage of microstate B were negatively correlated with the power of delta band in the stroke group, which did not pass adjustment (p = 0.033, adjusted p = 0.790, r = -0.436; p = 0.026, adjusted p = 0.790, r = -0.454, respectively). Our results confirm the abnormal temporal dynamics of brain activity in stroke patients. The study provides further electrophysiological evidence for understanding the mechanism of brain motor functional reorganization after stroke.

Zona Incerta GABAergic Neurons Facilitate Emergence from Isoflurane Anesthesia in Mice.

The zona incerta (ZI) predominantly consists of gamma-aminobutyric acid (GABAergic) neurons, located adjacent to the lateral hypothalamus. GABA, acting on GABAA receptors, serves as a crucial neuromodulator in the initiation and maintenance of general anesthesia. In this study, we aimed to investigate the involvement of ZI GABAergic neurons in the general anesthesia process. Utilizing in-vivo calcium signal optical fiber recording, we observed a decrease in the activity of ZI GABAergic neurons during isoflurane anesthesia, followed by a significant increase during the recovery phase. Subsequently, we selectively ablated ZI GABAergic neurons to explore their role in general anesthesia, revealing no impact on the induction of isoflurane anesthesia but a prolonged recovery time, accompanied by a reduction in delta-band power in mice under isoflurane anesthesia. Finally, through optogenetic activation/inhibition of ZI GABAergic neurons during isoflurane anesthesia, we discovered that activation of these neurons facilitated emergence without affecting the induction process, while inhibition delayed emergence, leading to fluctuations in delta band activity. In summary, these findings highlight the involvement of ZI GABAergic neurons in modulating the emergence of isoflurane anesthesia.

A study comparing brain wave patterns of fentanyl and ketamine in adult patients undergoing minimally invasive surgery.

This study aimed to investigate and compare the neurophysiological impacts of two widely used anesthetic agents, Fentanyl and Ketamine, on EEG power spectra during different stages of anesthesia in adult patients undergoing minimally invasive surgery. EEG data were collected from patients undergoing anesthesia with either Fentanyl or Ketamine. The data were analyzed for relative power spectrum and fast-to-slow wave power ratios, alongside Spectral Edge Frequency 95% (SEF95), at 3 key stages: pre-anesthesia, during stable anesthesia, and post-anesthesia. EEG Relative Power Spectrum: Initially, both groups exhibited similar EEG spectral profiles, establishing a uniform baseline (P > .05). Upon anesthesia induction, the Fentanyl group showed a substantial increase in delta band power (P < .05), suggesting deeper anesthesia, while the Ketamine group maintained higher alpha and beta band activity (P < .05), indicative of a lighter sedative effect. Fast and Slow Wave Power Ratios: The Fentanyl group exhibited a marked reduction in the fast-to-slow wave power ratio during anesthesia (P < .05), persisting post-anesthesia (P < .05) and indicating a lingering effect on brain activity. Conversely, the Ketamine group demonstrated a more stable ratio (P > .05), conducive to settings requiring rapid cognitive recovery. Spectral Edge Frequency 95% (SEF95): Analysis showed a significant decrease in SEF95 values for the Fentanyl group during anesthesia (P < .05), reflecting a shift towards lower frequency power. The Ketamine group experienced a less pronounced decrease (P > .05), maintaining a higher SEF95 value that suggested a lighter level of sedation. The study highlighted the distinct impacts of Fentanyl and Ketamine on EEG power spectra, with Fentanyl inducing deeper anesthesia as evidenced by shifts towards lower frequency activity and a significant decrease in SEF95 values. In contrast, Ketamine's preservation of higher frequency activity and more stable SEF95 values suggests a lighter, more dissociative anesthetic state. These findings emphasize the importance of EEG monitoring in anesthesia for tailoring anesthetic protocols to individual patient needs and optimizing postoperative outcomes.

Long-term impact of maternal prenatal smoking on EEG brain activity and internalizing/externalizing problem symptoms in young adults

ObjectiveThe objective of this study was to investigate the impact of smoking during pregnancy on the development of the child. While previous research has established its detrimental effects during early childhood, understanding potential long-term consequences into adulthood remains limited. This study specifically aimed to explore the influence of prenatal smoking exposure on brain activity and whether internalizing and externalizing symptoms are influenced by prenatal smoking exposure in a cohort of young adults. MethodsUtilizing data from 176 participants (mean age M = 24.68, SD = 0.49) and their mothers enrolled in a longitudinal risk study (MARS), we employed Generalized Additive Mixed Models (GAMMs) to analyze electroencephalography (EEG) power at rest and behavioral outcomes derived from the Young Adult-Self-Report (YASR) scales. Both covariate-unadjusted and −adjusted models were used, taking into account participant variables such as sex and age, as well as maternal factors like psychopathology and alcohol consumption, in addition to smoking and alcohol intake by the participants themselves. ResultsThe study revealed a significant impact of prenatal smoking on delta and theta band power, indicating decreased slower brain activity in prenatally exposed individuals compared to unexposed counterparts. Additionally, individuals exposed to prenatal smoking exhibited significantly higher levels of externalizing behavior. While this association was strongly influenced by maternal psychopathology, the child’s gender, and the child’s own substance use, the effect on delta power band remained after adjusting for covariates. ConclusionThe findings suggest that prenatal smoking exposure may have enduring effects on brain activity patterns in young adulthood. Conversely, the influence on externalizing behaviors depended on familial factors (maternal psychopathology) and the lifestyle of the individual (substance use).

Network signatures define consciousness state during focal seizures.

Epilepsy is a common neurological disorder affecting 1% of the global population. Loss of consciousness in focal impaired awareness seizures (FIASs) and focal-to-bilateral tonic-clonic seizures (FBTCSs) can be devastating, but the mechanisms are not well understood. Although ictal activity and interictal connectivity changes have been noted, the network states of focal aware seizures (FASs), FIASs, and FBTCSs have not been thoroughly evaluated with network measures ictally. We obtained electrographic data from 74 patients with stereoelectroencephalography (SEEG). Sliding window band power, functional connectivity, and segregation were computed on preictal, ictal, and postictal data. Five-minute epochs of wake, rapid eye movement sleep, and deep sleep were also extracted. Connectivity of subcortical arousal structures was analyzed in a cohort of patients with both SEEG and functional magnetic resonance imaging (fMRI). Given that custom neuromodulation of seizures is predicated on detection of seizure type, a convolutional neural network was used to classify seizure types. We found that in the frontoparietal association cortex, an area associated with consciousness, both consciousness-impairing seizures (FIASs and FBTCSs) and deep sleep had increases in slow wave delta (1-4 Hz) band power. However, when network measures were employed, we found that only FIASs and deep sleep exhibited an increase in delta segregation and a decrease in gamma segregation. Furthermore, we found that only patients with FIASs had reduced subcortical-to-neocortical functional connectivity with fMRI versus controls. Finally, our deep learning network demonstrated an area under the curve of .75 for detecting consciousness-impairing seizures. This study provides novel insights into ictal network measures in FASs, FIASs, and FBTCSs. Importantly, although both FIASs and FBTCSs result in loss of consciousness, our results suggest that ictal network changes in FIASs uniquely resemble those that occur during deep sleep. Our results may inform novel neuromodulation strategies for preservation of consciousness in epilepsy.

Analysis of taVNS effects on autonomic and central nervous systems in healthy young adults based on HRV, EEG parameters

Objective. Transcutaneous auricular vagus nerve stimulation (taVNS), a non-invasive method of stimulating the vagus nerve, simultaneously affects the autonomic nervous system (ANS) and central nervous system (CNS) through efferent and afferent pathways. The purpose of this study is to analyze the effect of taVNS on the ANS and CNS through heart rate variability (HRV) and electroencephalography (EEG) parameters of identified responders. Approach. Two sets of data were collected from each of 10 healthy adult male subjects in their 20 s, and five HRV parameters from the time domain (RMSSD, pNN50, pNN30, pNN20, ppNNx) and two EEG parameters (power of alpha band, power of delta band) were extracted. Main results. Based on pNN50, responders to taVNS were identified; among them, pNN50 (p = 0.0041) and ppNNx (p = 0.0037) showed significant differences before and after taVNS. At the same time, for alpha power and delta power of EEG, significant difference (p < 0.05) was observed in most channels after taVNS compared to before stimulation. Significance. This study demonstrated the validity of identifying responders using pNN50 and the influence of taVNS on both the ANS and CNS. We conclude that taVNS can be used to treat a variety of diseases and as a tool to help control the ANS and CNS.

Expanding the clinical application of OPM-MEG using an effective automatic suppression method for the dental brace metal artifact

Optically pumped magnetometer magnetoencephalography (OPM-MEG) holds significant promise for clinical functional brain imaging due to its superior spatiotemporal resolution. However, effectively suppressing metallic artifacts, particularly from devices such as orthodontic braces and vagal nerve stimulators remains a major challenge, hindering the wider clinical application of wearable OPM-MEG devices.A comprehensive analysis of metal artifact characteristics from time, frequency, and time–frequency perspectives was conducted for the first time using an OPM-MEG device in clinical medicine. This study focused on patients with metal orthodontics, examining the modulation of metal artifacts by breath and head movement, the incomplete regular sub-Gaussian distribution, and the high absolute power ratio in the 0.5–8 Hz band. The existing metal artifact suppression algorithms applied to SQUID-MEG, such as fast independent component analysis (FastICA), information maximization (Infomax), and algorithms for multiple unknown signal extraction (AMUSE), exhibit limited efficacy. Consequently, this study introduced the second-order blind identification (SOBI) algorithm, which utilized multiple time delays for the component separation of OPM-MEG measurement signals. We modified the time delays of the SOBI method to improve its efficacy in separating artifact components, particularly those in the ultralow frequency range. This approach employs the frequency-domain absolute power ratio, root mean square (RMS) value, and mutual information methods to automate the artifact component screening process.The effectiveness of this method was validated through simulation experiments involving four subjects in both resting and evoked experiments. In addition, the proposed method was also validated by the actual OPM-MEG evoked experiments of three subjects. Comparative analyses were conducted against the FastICA, Infomax, and AMUSE algorithms. Evaluation metrics included normalized mean square error, normalized delta band power error, RMS error, and signal-to-noise ratio, demonstrating that the proposed method provides optimal suppression of metal artifacts. This advancement holds promise for enhancing data quality and expanding the clinical applications of OPM-MEG.

Not Everyone Chooses Profit (If It Is too Tiring): What Behavioral and EEG Data Tell Us

Background: A more rewarding choice, even if it requires more effort, is usually preferred by individuals; yet, in some cases, individuals choose less profitable and less tiring options. This study explored the behavioral and electrophysiological (EEG) correlates of healthy adults performing a task, designed to investigate the decision-making process behind the selection of more effortful (but highly monetarily rewarding) and less effortful (but less monetarily rewarding) options. Methods: A sample of 20 healthy adults (mean age = 46.40) performed the decision-making task, while behavioral data and EEG frequency bands (delta, theta, alpha, and beta) were collected. The Maximization Scale (MS) was administered to evaluate individuals’ differences in the tendency to maximize their choices. Results: the results showed a general preference for selecting more compared to less effortful options, while no significant differences were obtained for the response times. Individuals who score higher on the MS High Standards subscale are more inclined to choose less effortful options; conversely, those with lower scores are more likely to choose a more effortful and rewarding option. However, no significant correlations were found between the behavioral data and the alternative search, or the decision difficulty subscales of the MS. EEG findings reported a significant interaction effect Choice × Electrode in delta, theta, alpha and beta bands. Specifically, the choice of less effortful options is associated with a higher increase in delta, theta, alpha, and beta band power in the right temporoparietal area (TP10) and by a lower activation of delta and theta in the contralateral site (TP9). The delta band decreased in left frontal area (AF7) during the task for the less versus more effortful options. Conclusions: Overall, despite more effortful and more monetarily rewarding options seeming to be the most rational ones to choose, less effortful choices are associated with specific EEG correlates, suggesting that there is a perceived advantage in avoiding automatisms, delaying gratification, and maximizing future possibilities.

Architectural Neuroimmunology: A Pilot Study Examining the Impact of Biophilic Architectural Design on Neuroinflammation

Recent research in architectural neuroscience has found that visual exposure to biophilic design may help reduce occupant physiological stress responses. However, there are still significant gaps in our understanding of the complex ways in which biophilic design impacts on building occupant neurophysiology. The relationship between visual exposure to biophilic design and neurophysiological responses such as neuroinflammation have yet to be directly investigated. This paper examines the results of a pilot study that was established to investigate the relationship between visual exposure to biophilic design and neuroinflammation, as mediated by physiological stress responses. The pilot study utilised a 32-channel quantitative electroencephalograph (qEEG) to assess the relative changes in neuroinflammatory markers (relative alpha and relative delta power band activity) of 10 participants while they were exposed to 2D digital images of buildings that visually expressed varying degrees of biophilic design. Participants exhibited a decrease in relative delta power when exposed to higher levels of biophilic design. No statistically significant changes in relative alpha power were observed. These findings suggest that exposure to buildings with higher degrees of biophilia may result in decreased neuroinflammatory activity. In doing so, this research works to further develop our understanding of the complex ways in which the built environment impacts on occupant neuroinflammation and physiological stress.

Unraveling the complexity of rapid eye movement microstates: insights from nonlinear EEG analysis.

Although rapid eye movement (REM) sleep is conventionally treated as a unified state, it comprises two distinct microstates: phasic and tonic REM. Recent research emphasizes the importance of understanding the interplay between these microstates, hypothesizing their role in transient shifts between sensory detachment and external awareness. Previous studies primarily employed linear metrics to probe cognitive states, such as oscillatory power, while in this study, we adopt Lempel-Ziv Complexity (LZC), to examine the nonlinear features of electroencephalographic (EEG) data from the REM microstates and to gain complementary insights into neural dynamics during REM sleep. Our findings demonstrate a noteworthy reduction in LZC during phasic REM compared to tonic REM states, signifying diminished EEG complexity in the former. Additionally, we noted a negative correlation between decreased LZC and delta band power, along with a positive correlation with alpha band power. This study highlights the potential of nonlinear EEG metrics, particularly LZC, in elucidating the distinct features of REM microstates. Overall, this research contributes to advancing our understanding of the complex dynamics within REM sleep and opens new avenues for exploring its implications in both clinical and nonclinical contexts.

Imagined speech classification exploiting EEG power spectrum features.

Imagined speech recognition has developed as a significant topic of research in the field of brain-computer interfaces. This innovative technique has great promise as a communication tool, providing essential help to those with impairments. An imagined speech recognition model is proposed in this paper to identify the ten most frequently used English alphabets (e.g., A, D, E, H, I, N, O, R, S, T) and numerals (e.g., 0 to 9). A novel electroencephalogram (EEG) dataset was created by measuring the brain activity of 30 people while they imagined these alphabets and digits. As part of signal preprocessing, EEG signals are filtered before extracting delta, theta, alpha, and beta band power features. These features are used as input for classification using support vector machines, k-nearest neighbors, and random forest (RF) classifiers. It is identified that the RF classifier outperformed the others in terms of classification accuracy. Classification accuracies of 99.38% and 95.39% were achieved at the coarse-level and fine-level, respectively with the RF classifier. From our study, it is also revealed that the beta frequency band and the frontal lobe of the brain played crucial roles in imagined speech recognition. Furthermore, a comparative analysis against state-of-the-art techniques is conducted to demonstrate the efficacy of our proposed model.

Prediction error in implicit adaptation during visually- and memory-guided reaching tasks

Human movements are adjusted by motor adaptation in order to maintain their accuracy. There are two systems in motor adaptation, referred to as explicit or implicit adaptation. It has been suggested that the implicit adaptation is based on the prediction error and has been used in a number of motor adaptation studies. This study aimed to examine the effect of visual memory on prediction error in implicit visuomotor adaptation by comparing visually- and memory-guided reaching tasks. The visually-guided task is thought to be implicit learning based on prediction error, whereas the memory-guided task requires more cognitive processes. We observed the adaptation to visuomotor rotation feedback that is gradually rotated. We found that the adaptation and retention rates were higher in the visually-guided task than in the memory-guided task. Furthermore, the delta-band power obtained by electroencephalography (EEG) in the visually-guided task was increased immediately following the visual feedback, which indicates that the prediction error was larger in the visually-guided task. Our results show that the visuomotor adaptation is enhanced in the visually-guided task because the prediction error, which contributes update of the internal model, was more reliable than in the memory-guided task. Therefore, we suggest that the processing of the prediction error is affected by the task-type, which in turn affects the rate of the visuomotor adaptation.

482 Identification of Candidate Neural Biomarkers of Obsessive-compulsive Symptom Intensity and Response to DBS

INTRODUCTION: Despite the success of DBS for the treatment of refractory OCD, there are currently no robust neural signatures for obsessive-compulsive (OC) symptoms or initial mood and energy improvements associated with DBS. This gap may be due to limited opportunities available for conducting intracranial electrophysiological recordings during natural symptom fluctuations. Recently available DBS platforms offer a way over this hurdle, allowing for streaming of intracranial local field potentials (LFP) at home and in the clinic. METHODS: We conducted longitudinal intracranial recordings in 10 patients with refractory OCD implanted with recording-capable DBS devices targeted to ventral capsule/ventral striatum (VC/VS). Five of the 10 were additionally implanted with ECoG strip electrodes over the orbitofrontal cortex (OFC). We captured LFP at home during naturalistic exposures to OCD triggers and in the clinic during variations in stimulation amplitude. RESULTS: All 5 participants who have completed the study so far are clinical responders to DBS therapy. Using the intracranial data collected during OCD exposures, we identified low delta-band power as a candidate neural biomarker of OC symptom intensity during symptom provocations (left VC/VS: R = -0.59, p = 0.01; right VC/VS: R = -0.56, p = 0.04). Electrophysiological analysis of acute response to stimulation revealed a narrow band increase in 30 Hz power specific to lateral OFC that was associated with increased talkativeness and approach behaviors that are typical harbingers of clinical response CONCLUSIONS: These signals have potential utility for classification of symptom intensity and increased mood and energy to enable adaptive DBS systems for OCD. Our data suggest that the neural biomarkers of these two clinical features might be separable, thus possibly allowing for simultaneous optimization of both.

Elevated pain sensitivity is associated with reduced rapid eye movement (REM) sleep in females with comorbid temporomandibular disorder and insomnia.

Patients with chronic pain disorders, including Temporomandibular Disorders (TMDs) endorse high levels of sleep disturbances, frequently reporting reduced sleep quality. Despite this, little is known about the effect that daytime pain has on the microstructure and macro-architecture of sleep. Therefore, we aimed to examine the extent to which daytime pain sensitivity, measured using quantitative sensory testing (QST), is associated with objective sleep parameters the following night, including sleep architecture and power spectral density, in women with TMD. 144 females with myalgia and arthralgia by examination using the Diagnostic criteria for TMD completed a comprehensive QST battery consisting of General Pain Sensitivity, Central Sensitization Index, and Masseter Pressure Pain Threshold assessments. Polysomnography was collected the same night to measure sleep architecture and calculate relative power in delta, theta, alpha, sigma, and beta power bands. Central Sensitization (B = -3.069, P = .009), General Pain Sensitivity Indices (B = -3.069, P = .007), and Masseter Pain Pressure Threshold (B = 0.030, P = .008) were significantly associated with lower REM% both before and after controlling for covariates. Pain sensitivity measures were not significantly associated with relative power in any of the spectral bands nor with any other sleep architectural stages. Our findings demonstrate that higher generalized pain sensitivity, masseter pain pressure threshold, as well as central sensitization were associated with a lower percentage of REM in participants with myofascial pain and arthralgia of the masticatory system. These findings provide an important step toward understanding the mechanistic underpinnings of how chronic pain interacts with sleep physiology.

Sex differences in body temperature and neural power spectra in response to repeated restraint stress

Repeated stress is associated with an increased risk of developing psychiatric illnesses such as post-traumatic stress disorder (PTSD), which is more common in women, yet the neurobiology behind this sex difference is unknown. Habituation to repeated stress is impaired in PTSD, and recent preclinical studies have shown that female rats do not habituate as fully as male rats to repeated stress, which leads to impairments in cognition and sleep. Further research should examine sex differences after repeated stress in other relevant measures, such as body temperature and neural activity. In this study, we analyzed core body temperature and EEG power spectra in adult male and female rats during restraint, as well as during sleep transitions following stress. We found that core body temperature of male rats habituated to repeated restraint more fully than female rats. Additionally, we found that females had a higher average beta band power than males on both days of restraint, indicating higher levels of arousal. Lastly, we observed that females had lower delta band power than males during sleep transitions on Day 1 of restraint, however, females demonstrated higher delta band power than males by Day 5 of restraint. This suggests that it may take females longer to initiate sleep recovery compared with males. These findings indicate that there are differences in the physiological and neural processes of males and females after repeated stress. Understanding the way that the stress response is regulated in both sexes can provide insight into individualized treatment for stress-related disorders.

Aberrant Cortical Activity in 5xFAD Mice in Response to Social and Non-Social Olfactory Stimuli.

Neuroimaging studies investigating the behavioral and psychological symptoms of dementia (BPSD)- such as apathy, anxiety, and depression- have linked some of these symptoms with altered neural activity. However, inconsistencies in operational definitions and rating scales, limited scope of assessments, and poor temporal resolution of imaging techniques have hampered human studies. Many transgenic (Tg) mouse models of Alzheimer's disease (AD) exhibit BPSD-like behaviors concomitant with AD-related neuropathology, allowing examination of how neural activity may relate to BPSD-like behaviors with high temporal and spatial resolution. To examine task-dependent neural activity in the medial prefrontal cortex (mPFC) of AD-model mice in response to social and non-social olfactory stimuli. We previously demonstrated age-related decreases in social investigation in Tg 5xFAD females, and this reduced social investigation is evident in Tg 5xFAD females and males by 6 months of age. In the present study, we examine local field potential (LFP) in the mPFC of awake, behaving 5xFAD females and males at 6 months of age during exposure to social and non-social odor stimuli in a novel olfactometer. Our results indicate that Tg 5xFAD mice exhibit aberrant baseline and task-dependent LFP activity in the mPFC- including higher relative delta (1-4 Hz) band power and lower relative power in higher bands, and overall stronger phase-amplitude coupling- compared to wild-type controls. These results are consistent with previous human and animal studies examining emotional processing, anxiety, fear behaviors, and stress responses, and suggest that Tg 5xFAD mice may exhibit altered arousal or anxiety.

Predicting the therapeutic response to valproic acid in childhood absence epilepsy through electroencephalogram analysis using machine learning

Childhood absence epilepsy (CAE) is a common type of idiopathic generalized epilepsy, manifesting as daily multiple absence seizures. Although seizures in most patients can be adequately controlled with first-line antiseizure medication (ASM), approximately 25 % of patients respond poorly to first-line ASM. In addition, an accurate method for predicting first-line medication responsiveness is lacking. We used the quantitative electroencephalogram (QEEG) features of patients with CAE along with machine learning to predict the therapeutic effects of valproic acid in this population. We enrolled 25 patients with CAE from multiple medical centers. Twelve patients who required additional medication for seizure control or who were shifted to another ASM and 13 patients who achieved seizure freedom with valproic acid within 6 months served as the nonresponder and responder groups. Using machine learning, we analyzed the interictal background EEG data without epileptiform discharge before ASM. The following features were analyzed: EEG frequency bands, Hjorth parameters, detrended fluctuation analysis, Higuchi fractal dimension, Lempel–Ziv complexity (LZC), Petrosian fractal dimension, and sample entropy (SE). We applied leave-one-out cross-validation with support vector machine, K-nearest neighbor (KNN), random forest, decision tree, Ada boost, and extreme gradient boosting, and we tested the performance of these models. The responders had significantly higher alpha band power and lower delta band power than the nonresponders. The Hjorth mobility, LZC, and SE values in the temporal, parietal, and occipital lobes were higher in the responders than in the nonresponders. Hjorth complexity was higher in the nonresponders than in the responders in almost all the brain regions, except for the leads FP1 and FP2. Using KNN classification with theta band power in the temporal lobe yielded optimal performance, with sensitivity of 92.31 %, specificity of 76.92 %, accuracy of 84.62 %, and area under the curve of 88.46 %.We used various EEG features along with machine learning to accurately predict whether patients with CAE would respond to valproic acid. Our method could provide valuable assistance for pediatric neurologists in selecting suitable ASM.

Toward Objectification of Subjective Chronic Pain based on Implicit Response in Biosignals.

Chronic pain necessitates early intervention and accurate evaluation. Current subjective questionnaire -based methods have limitations. This study aims to develop a chronic pain assessment method based on multi-modal biosignal and to validate its feasibility. We present a model utilizing electroencephalogram (EEG), photoplethysmogram (PPG), electrocardiogram (ECG), and facial temperature (FT) data from 59 subjects (26 chronic pain patients). A total of 112 features were derived from all signals, and 17 of them showed a significant difference between the chronic pains and the normal control. By optimizing signal types and feature combinations, our pain classification model significantly enhanced chronic pain assessment (AUROC: 0.802 to 0.864). Notable features included PPG systolic length (12.3%), EEG alpha band power (11.1%), and delta band power (9.4%). This multi-modal biosignal approach holds promise for effective chronic pain quantification.

Real‐time Memory‐related Neurofeedback Training Improves Intrinsic Brain Oscillations in Older Adults

AbstractBackgroundMild cognitive impairment (MCI), manifesting as working memory deficits for example, is the most common and earliest symptom of Alzheimer’s disease and related dementias (AD/ADRD). To combat the brain aging processes, early intervention is needed. Electroencephalogram (EEG)‐based neurofeedback (NF) has been found to be beneficial in treating a variety of brain disorders. Increased intrinsic delta power and decreased alpha is associated with brain aging and AD pathologies. We have developed an ERP‐based NF protocol to reward memory‐related potentials. In this study, we aim to evaluate the effect of NF training protocol.MethodThree older males (ages, 63, 78, and 79) participated in a newly developed NF training protocol including baseline and memory‐related NF training (twice a week). Each participant was recorded using 90‐second of eyes closed (EC) before multiple NF sessions during each visit. Their brain signals were collected with a 14‐channel wireless electroencephalogram (EEG) headset and processed in real‐time. The NF training contained 2 sham sessions followed by 2 intervention sessions. Compared to the intervention, visual feedback in the sham was provided in random order rather than triggered by participants’ ERP fluctuations. The EEG signal collected with EC before the experiment (Baseline, T0), 3 days after the sham (Sham, T1), and 3 days after the intervention (post‐intervention, T2) were preprocessed as 4s epoch windows and computed as the delta, theta, alpha, beta, and gamma band power. The average EEG band powers from T0, T1, and T2 and memory performances from the three participants were calculated.ResultThe working memory performance (accuracy and reaction times during retrievals of memory target and rejection of nontarget) did not show significant change among the three participants. In contrast, the EC intrinsic delta and alpha power changed towards brainwaves seen in younger brains after several sessions of NF training. Additionally, sham also improves in the alpha band (associated with attention and vigilance).ConclusionThe pilot results demonstrated the effectiveness of a newly developed NF training using working memory related potentials in two out of three older adults. The results indicate the promise of cognitive enhancement using event‐related potentials‐based NF training in real time.