Brain Waves Research Articles

Brain electrical activity in female Major Depressive Disorder patients

Background: Quantitative EEG provides insight to the brain activity associated with depressive illness. Frontal asymmetry and changes of slow wave frequencies brain activity have been found as the characteristic features of Major Depressive Disorder (MDD). Objective: To assess the brain activity by power spectral analysis of EEG in female patients with MDD. Methods: This observational study was carried on 20 female MDD patients (age 20-40 years) enrolled from the Department of Psychiatry, Bangabandhu Sheikh Mujib Medical University, Shahbag, Dhaka. Twenty (20) healthy volunteers of the same age were considered the control group. For the study, EEG was recorded for 3 minutes in eye closed position by EEG data acquisition device, EEG Traveler BrainTech 32+ CMEEG-01, India. Power spectral analysis of EEG data was done by BT40 analysis software. For statistical analysis, the Mann-Whitney U test was done. Results: Significantly higher Absolute power of delta and theta brain wave, and lower alpha and beta brain wave activity was observed in female MDD patients compared to control in all most all electrodes in all cortical regions. Conclusion: MDD patients showed low excitable brain state and characteristic feature of high slow frequency wave and low fast frequency wave in MDD which can be diagnosed by quantitative EEG analysis. J Bangladesh Soc Physiol 2023;18(2): 53-62

SynchroLINNce: toolbox for Neural Synchronization and Desynchronization Assessment in Epilepsy Animal Models

Epilepsy is a worldwide public health issue, given its biological, social, and economic impacts. Considering several open questions about synchronization and desynchronization mechanisms underlying epileptic phenomena, the development of algorithms and computational toolboxes for such analysis is highly relevant to their research. Moreover, given the recent developments of neurotechnology for epilepsy, it is essential to understand that proposals like computational tools may provide consistent data for closed-loop control systems, necessary in neuromodulation treatment alternatives, and for real-time monitoring systems to predict the occurrence of epileptic seizures. In the present work, SynchroLINNce, a freely distributable MATLAB toolbox designed to be used by epilepsy neuroscientists, including software-untrained), is proposed. Among its features, several functionalities such as recording visualization, digital filtering, and correlation analysis, as well as more specific methodologies, such as mechanisms for the automatic detection of epileptiform spikes, morphology analysis of these spikes, and their coincidence between channels are presented.

Brain electrical activity in Newly diagnosed Generalized Anxiety Disorder: A Quantitative EEG analysis

Background: Generalized Anxiety Disorder (GAD) is one of the most common global mental disorders with negative impact on social life. Quantitative EEG can objectively estimate the anxiety status. Relatively increase beta activity along with decrease alpha activity is associated with GAD. Objectives:To evaluate power of all brain waves in female patients with GAD. Methods:This observational study was conducted on 20 female patients with GAD (age 20-40 years),For comparison, 20 healthy female volunteers of same age were enrolled as control. In this study, EEG was recorded for 3 minutes by placing electrodes on scalp according to 10-20 system. To assess brain electrical activities, the band power of delta, theta, alpha and beta brainwave was recorded from an EEG data acquisition device, EEG Traveler Brain Tech 32+ CMEEG -01, India. Power spectral analysis of EEG data was done by BT40 analysis software. Data were expressed as median (Inter Quartile Range). Statistical analysis was done by Mann-Whitney U test.Results: The significantly higher absolute power of beta was found in most of the electrodes of prefrontal, frontal and temporal region as well as significantly lower power of alpha, theta and delta werefound in all cortical regions in female GAD patients compared to healthy controls at baseline. Conclusion: This study concluded that GAD patients had high excitable state in most of cortical region associated with lack of relaxing ability. J Bangladesh Soc Physiol 2023;18(2): 78-88

Cortical oscillations and event-related brain potentials during the preparation and execution of deceptive behavior.

Deception often occurs in response to a preceding cue (e.g., a precarious question) alerting us about the need to subsequently lie. Here, we simulate this process by adapting a previously established paradigm of intentionally false responding, now instructing participants about the need for deception (vs. truthful responses) by means of a simple cue occurring before each response-relevant target. We analyzed event-related brain potentials (ERPs) as well as cortical oscillations recorded from the scalp. In an experimental study (N = 44), we show that a cue signaling the need for deception involves increased attentional selection (P2, P3a, P3b). Moreover, in the period following the cue and leading up to the target, ERP and oscillatory signatures of anticipation and preparation (Contingent Negative Variation, alpha suppression) were found to be increased during trials requiring a deceptive as compared to a truthful response. Additionally, we replicated earlier findings that target processing involves enhanced motivated attention toward words requiring a deceptive response (LPC). Moreover, a signature of integration effort and semantic inhibition (N400) was observed to be larger for words to which responses have to be intentionally false as compared to those to which responses must be truthful. Our findings support the view of the involvement of a series of basic cognitive processes (especially attention and cognitive control) when responses are deliberately wrong instead of right. Moreover, preceding cues signaling the subsequent need for lying already elicit attentional and preparatory mechanisms facilitating the cognitive operations necessary for later successful lying.

Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks.

Understanding cognitive processes in the brain demands sophisticated models capable of replicating neural dynamics at large scales. We present a physiologically inspired speech recognition architecture, compatible and scalable with deep learning frameworks, and demonstrate that end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Significant cross-frequency couplings, indicative of these oscillations, are measured within and across network layers during speech processing, whereas no such interactions are observed when handling background noise inputs. Furthermore, our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronizing neural activity to improve recognition performance. Overall, on top of developing our understanding of synchronization phenomena notably observed in the human auditory pathway, our architecture exhibits dynamic and efficient information processing, with relevance to neuromorphic technology.

Dynamics of brain wave rhythms predicts the speed of cognitive tasks

We investigated the physiological and behavioral characteristics of children aged 9—10 years during a neurophysiological experiment aimed at performing tasks based on the Sternberg paradigm. A statistical analysis of the stability of wave rhythms as well as a correlation analysis between them and the average reaction time was carried out, based on which a biomarker of the efficiency of the working memory task was found.

Interaction of motor behaviour, cortical oscillations and deep brain stimulation in Parkinson disease

Abstract Recent progress in the study of Parkinson's disease (PD) has highlighted the pivotal role of beta oscillations within the basal ganglia-thalamo-cortical network in modulating motor symptoms. Predominantly manifesting as transient bursts, these beta oscillations are central to the pathophysiology of PD motor symptoms, especially bradykinesia. Our central hypothesis is that increased bursting duration in cortex, coupled with kinematics of movement, disrupts the typical flow of neural information, leading to observable changes in motor behavior in PD. To explore this hypothesis, we employed an integrative approach, analyzing the interplay between moment-to-moment brain dynamics and movement kinematics, and the modulation of these relationships by therapeutic deep brain stimulation (DBS). Local field potentials were recorded from the hand motor (M1) and premotor cortical (PM) areas, and internal Globus Pallidus (GPi) in 26 PD patients undergoing DBS implantation surgery. Participants executed rapid alternating hand movements in 30-second blocks, both with and without therapeutic pallidal stimulation. Behaviorally, the analysis revealed bradykinesia, with hand movement cycle width increasing linearly over time during DBS-OFF blocks. Crucially, there was a moment-to-moment correlation between M1 low beta burst duration and movement cycle width, a relationship that dissipated with therapeutic DBS. Further analyses suggest that high gamma activity correlates with enhanced motor performance with DBS-ON. Regardless of the nature of coupling, DBS’s modulation of cortical bursting activity appeared to amplify the brain signals’ informational content regarding instantaneous movement changes. Our findings underscore that DBS significantly reshapes the interaction between motor behavior and neural signals in PD, not only modulating specific bands but also expanding the system's capability to process and relay information for motor control. These insights shed light on the possible network mechanisms underlying DBS therapeutic effects, suggesting a profound impact on both neural and motor domains.

Machine Learning-Based Approach: Enhancing Social Media Platform

Machine Learning is a technique that aims to learn from data over a task based on certain performance measures. With the flooding of data and increased computational ability, machine learning algorithms have a huge research scope. The task of identifying the correct machine learning algorithm for an application is very challenging without analysing the basic information about the problem such as the domain of the undertaken problem, the features available in the data, etc. The movie industry, a huge part of entertainment industry, has seen a phenomenal growth throughout the globe in recent times. A movie can capture the attention of a viewer and can trigger cognitive and emotional processes in the brain. In this research, the emotional outcome of the viewer was analysed while they watch the movie before its actual release that is, during its preview. Traditionally Functional Magnetic Resonance Imaging device was used to assess human brain activity but proved to be non-feasible and costly so EEG sensors were used to monitor and record the functioning of the brain of volunteers for further analysis. We proposed a model to use the collected data through EEG sensors were analysed using artificial neural network which was Used to find high and low of different brain waves mapping to the emotions depicted in every scene of the movie. Performance measures such as accuracy, precision and recall were calculated to validate our proposed model. Our proposed model resulted in providing assistance to movie makers who could Study the pulse of audience before the actual release and could incorporate changes if necessary.

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.

Experience Dependence of Alpha Rhythms and Neural Dynamics in the Mouse Visual Cortex.

The role of experience in the development and maintenance of emergent network properties such as cortical oscillations and states is poorly understood. To define how early-life experience affects cortical dynamics in the visual cortex of adult, head-fixed mice, we examined the effects of two forms of blindness initiated before eye opening and continuing through recording: (1) bilateral loss of retinal input (enucleation) and (2) degradation of visual input (eyelid suture). Neither form of deprivation fundamentally altered the state-dependent regulation of firing rates or local field potentials. However, each deprivation caused unique changes in network behavior. Laminar analysis revealed two different generative mechanisms for low-frequency synchronization: one prevalent during movement and the other during quiet wakefulness. The former was absent in enucleated mice, suggesting a mouse homolog of human alpha oscillations. In addition, neurons in enucleated animals were less correlated and fired more regularly, but no change in mean firing rate. Eyelid suture decreased firing rates during quiet wakefulness, but not during movement, with no effect on neural correlations or regularity. Sutured animals showed a broadband increase in depth EEG power and an increased occurrence, but reduced central frequency, of narrowband gamma oscillations. The complementary-rather than additive-effects of lid suture and enucleation suggest that the development of emergent network properties does not require vision but is plastic to modified input. Our results suggest a complex interaction of internal set points and experience determines mature cortical activity, with low-frequency synchronization being particularly susceptible to early deprivation.

Auditory areas are recruited for naturalistic visual meaning in early deaf people

Congenital deafness enhances responses of auditory cortices to non-auditory tasks, yet the nature of the reorganization is not well understood. Here, naturalistic stimuli are used to induce neural synchrony across early deaf and hearing individuals. Participants watch a silent animated film in an intact version and three versions with gradually distorted meaning. Differences between groups are observed in higher-order auditory cortices in all stimuli, with no statistically significant effects in the primary auditory cortex. Comparison between levels of scrambling revealed a heterogeneity of function in secondary auditory areas. Both hemispheres show greater synchrony in the deaf than in the hearing participants for the intact movie and high-level variants. However, only the right hemisphere shows an increased inter-subject synchrony in the deaf people for the low-level movie variants. An event segmentation validates these results: the dynamics of the right secondary auditory cortex in the deaf people consist of shorter-length events with more transitions than the left. Our results reveal how deaf individuals use their auditory cortex to process visual meaning.

Cortical oscillations are modified by expertise in dance and music: Evidence from live dance audience.

Over the past decades, the focus of brain research has expanded from using strictly controlled stimuli towards understanding brain functioning in complex naturalistic contexts. Interest has increased in measuring brain processes in natural interaction, including classrooms, theatres, concerts and museums to understand the brain functions in the real world. Here, we examined how watching a live dance performance with music in a real-world dance performance setting engages the brains of the spectators. Expertise in dance or music has been shown to modify brain functions, including when watching dance or listening to music. Therefore, we recorded electroencephalography (EEG) from an audience of dancers, musicians and novices as they watched the live dance performance and analysed their cortical oscillations. We compared intrabrain oscillations when participants watched the performance (with music) or listened to the music alone without the dance. We found that dancers have stronger fronto-central and parieto-occipital theta phase synchrony (4-8Hz) than novices when watching dance, likely reflecting the effects of dance experience on motor imagery, multisensory and social interaction processes. Also, compared with novices, dancers had stronger delta phase synchrony (0.5-4Hz) when listening to music, and musicians had stronger delta phase synchrony when watching dance, suggesting expertise in music and dance enhances sensitivity or attention to temporal regularities in movement and sound.

Performance analysis of the FB-PLL applied to real time monitoring of deep sleep brainwaves

Repose is indispensable for the human body, one of its main functions being to replenish energy. During sleep, the body's important restorative activities are performed by the organism, which is a basic human need. With awareness of the dimension that sleep represents and the need to improve its quality, the importance of monitoring in real time becomes evident. Based on this, the fast fourier transform is a technique capable of obtaining characteristics of a signal, such as its frequency, as well as being able to be applied to sleep waves. However, to have a good use, a considerable number of sample experimental cycles is necessary, which demands a certain computational effort. In this sense, this work we sought to apply the Phase-Locked Loop technique. Thus, a Fourier-based synchronization algorithm is proposed to track low-frequency brain waves during sleep. It is also worth noting that this method is capable of performing the estimates in just one cycle. Therefore, the objective is to obtain the estimated curve referring to the frequency and an efficient synchronization algorithm that is able to track deep sleep in real time.

Differential effects of haloperidol on neural oscillations during wakefulness and sleep

The electrical activity of the brain, characterized by its frequency components, reflects a complex interplay between periodic (oscillatory) and aperiodic components. These components are associated with various neurophysiological processes, such as the excitation-inhibition balance (aperiodic activity) or interregional communication (oscillatory activity). However, we do not fully understand whether these components are truly independent or if different neuromodulators affect them in different ways. The dopaminergic system has a critical role for cognition and motivation, being a potential modulator of these power spectrum components. To improve our understanding of these questions, we investigated the differential effects of this system on these components using electrocorticogram recordings in cats, which show clear oscillations and aperiodic 1/f activity. Specifically, we focused on the effects of haloperidol (a D2 receptor antagonist) on oscillatory and aperiodic dynamics during wakefulness and sleep. By parameterizing the power spectrum into these two components, our findings reveal a robust modulation of oscillatory activity by the D2 receptor across the brain. Surprisingly, aperiodic activity was not significantly affected and exhibited inconsistent changes across the brain. This suggests a nuanced interplay between neuromodulation and the distinct components of brain oscillations, providing insights into the selective regulation of oscillatory dynamics in awake states.

Mindfulness meditation alters neural oscillations independently of arousal

Neuroscience has identified that mindfulness meditation induces a state of relaxed alertness, characterised by changes in theta and alpha oscillations and reduced sympathetic arousal, although the underlying mechanisms remain unclear. This study aims to address this gap by examining changes in neural oscillations and arousal during mindfulness meditation using both traditional and data-driven methods. Fifty-two healthy young adults underwent electroencephalography (EEG) and skin conductance level (SCL) recordings during resting baseline and mindfulness meditation conditions, both conducted with eyes closed. The EEG data revealed a significant decrease in traditional alpha (8–13 Hz) amplitude during mindfulness meditation compared to rest. However, no significant differences were observed between conditions in traditional delta, theta, beta, or gamma amplitudes. Frequency Principal Components Analysis (fPCA) was employed as a data-driven approach, identifying six components consistent across conditions. A complex delta-theta-alpha component significantly increased during mindfulness meditation. In contrast, low alpha (~9.5 Hz) and low alpha-beta (~11 Hz) components decreased significantly during mindfulness meditation. No significant differences were observed between conditions in the delta, high alpha, and high alpha-beta components. Additionally, there were no significant differences in SCL between conditions, nor were there correlations between traditional alpha or fPCA components and SCL. These findings support the conceptualisation of mindfulness meditation as a state of relaxed alertness, characterised by changes in neural oscillations likely associated with attention and awareness. However, the observed changes do not appear to be driven by arousal.

Cochlear implant induced changes in cortical networks associated with tinnitus severity

We investigated tinnitus-related cortical networks in cochlear implant users who experience tinnitus and whose perception of tinnitus changes with use of their implant. &#xD;&#xD;Tinnitus, the perception of unwanted sounds which are not present externally, can be a debilitating condition. In individuals with cochlear implants, use of the implant is known to modulate tinnitus, often improving symptoms but worsening them in some cases. Little is known about underlying cortical changes with use of the implant, which lead to changes in tinnitus perception. In this study we investigated whether changes in brain networks with the cochlear implant turned on and off, were associated with changes in tinnitus perception, as rated subjectively.&#xD;&#xD;Approach: Using functional near-infrared spectroscopy (fNIRS), we recorded cortical activity at rest, from 14 cochlear implant users who experienced tinnitus. Recordings were performed with the cochlear implant turned off and on. For each condition, participants rated the loudness and annoyance of their tinnitus using a visual rating scale. Changes in neural synchrony have been reported in humans and animal models of tinnitus. To assess neural synchrony, functional connectivity networks with the implant turned on and off, were compared using two network features: node strength and diversity coefficient. &#xD;&#xD;Main results: Changes in subjective ratings of loudness were significantly correlated with changes in node strength, averaged across occipital channels (r=-0.65,p = 0.01). Changes in both loudness and annoyance were significantly correlated with changes in diversity coefficient averaged across all channels (r=-0.79,p<0.001 and r = -0.86,p<0.001). More distributed connectivity with the implant on, compared to implant off, was associated with a reduction in tinnitus loudness and annoyance. &#xD;&#xD;Significance: A better understanding of neural mechanisms underlying tinnitus suppression with cochlear implant use, could lead to their application as a tinnitus treatment and pave the way for effective use of other less invasive stimulation-based treatments. &#xD.

Constructing the "Family Personality": Can Family Functioning Be Linked to Parent-Child Interpersonal Neural Synchronization?

Early child development occurs within an interactive environment, initially dominated by parents or caregivers, and is heavily influenced by the dynamics of this social context. The current study probed the neurobiology of "family personality", or family functioning, in the context of parent-child dyadic interaction using a two-person neuroimaging modality. One hundred and five parent-child dyads (child mean age 5 years 4 months) were recruited. Functional near-infrared spectroscopy (fNIRS) hyperscanning was employed to measure neural synchrony while dyads completed a mildly stressful interactive task. Family functioning was measured through the Family Adaptability and Cohesion Scale IV (FACES-IV). Synchrony during stress was significantly greater than synchrony during both baseline and recovery conditions for all dyads. A significant interaction between neural synchrony in each task condition and familial balanced flexibility was found, such that higher levels of balanced flexibility were associated with greater changes in frontal cortex neural synchrony as dyads progressed through the three task conditions. Parent-child dyads from families who display heightened levels of balanced flexibility are also more flexible in their engagement of neural synchrony when shifting between social conditions. This is one of the first studies to utilize a two-person imaging modality to explore the links between family functioning and interbrain synchrony between parents and their children.

Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics.

Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/f noise within the power spectrum fail to distinguish between the fundamental frequency and harmonics of often highly non-sinusoidal neural oscillations. To overcome this limitation, we define fundamental criteria that characterize neural oscillations and introduce the cyclic homogeneous oscillation (CHO) detection method. We implemented these criteria based on an autocorrelation approach to determine an oscillation's fundamental frequency. We evaluated CHO by verifying its performance on simulated non-sinusoidal oscillatory bursts and validated its ability to determine the fundamental frequency of neural oscillations in electrocorticographic (ECoG), electroencephalographic (EEG), and stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.

Partisans process policy-based and identity-based messages using dissociable neural systems.

Political partisanship is often conceived as a lens through which people view politics. Behavioral research has distinguished two types of "partisan lenses"-policy-based and identity-based-that may influence peoples' perception of political events. Little is known, however, about the mechanisms through which partisan discourse appealing to policy beliefs or targeting partisan identities operate within individuals. We addressed this question by collecting neuroimaging data while participants watched videos of speakers expressing partisan views. A "partisan lens effect" was identified as the difference in neural synchrony between each participant's brain response and that of their partisan ingroup vs. outgroup. When processing policy-based messaging, a partisan lens effect was observed in socio-political reasoning and affective responding brain regions. When processing negative identity-based attacks, a partisan lens effect was observed in mentalizing and affective responding brain regions. These data suggest that the processing of political discourse that appeals to different forms of partisanship is supported by related but distinguishable neural-and therefore psychological-mechanisms, which may have implications for how we characterize partisanship and ameliorate its deleterious impacts.

Spatial prediction modulates the rhythm of attentional sampling.

Recent studies demonstrate that behavioral performance during visual spatial attention fluctuates at theta (4 to 8Hz) and alpha (8 to 16Hz) frequencies, linked to phase-amplitude coupling of neural oscillations within the visual and attentional system depending on task demands. To investigate the influence of prior spatial prediction, we employed an adaptive discrimination task with variable cue-target onset asynchronies (300 to 1,300ms) and different cue validity (100% & 50%). We recorded electroencephalography concurrently and adopted adaptive electroencephalography data analytical methods, namely, Holo-Holo-Hilbert spectral analysis and Holo-Hilbert cross-frequency phase clustering. Our findings indicate that response precision for near-threshold Landolt rings fluctuates at the theta band (4Hz) under certain predictions and at alpha & beta bands (15 & 19Hz) with uncertain predictions. Furthermore, spatial prediction strengthens theta-alpha modulations at parietal-occipital areas, frontal theta/parietal-occipital alpha phase-amplitude coupling, and within frontal theta-alpha phase-amplitude coupling. Notably, during the pretarget period, beta-modulated gamma oscillations in parietal-occipital areas predict response precision under uncertain prediction, while frontal theta/parietal-occipital alpha phase-amplitude coupling predicts response precision in spatially certain conditions. In conclusion, our study highlights the critical role of spatial prediction in attentional sampling rhythms with both behavioral and electroencephalography evidence.