Beta Power Research Articles

Theta and beta power in the subthalamic nucleus respond to conflict across subregion and hemisphere

Abstract The subthalamic nucleus is thought to play a crucial role in controlling impulsive actions. Networked among the basal ganglia and receiving input from several cortical areas, the subthalamic nucleus is well-positioned to influence action selection when faced with competing and conflicting action outcomes. The purpose of this study was to test the dissociable roles of the dorsal and ventral aspects of the subthalamic nucleus during action conflict in patients with Parkinson’s disease undergoing intraoperative neurophysiological recording and to explore a potential mechanism for this inhibitory control. We hypothesized that modulations of neurophysiological activity during action conflict would be more pronounced in the dorsal subthalamic nucleus compared to the ventral subthalamic nucleus, due to the dissociation of cortical afferents to subthalamic nucleus subregions and previous findings of deep brain stimulation targeting subthalamic nucleus subregions in Parkinson’s disease. We recorded neurophysiological activity while ten participants with Parkinson’s disease performed the Simon task during deep brain stimulation surgery. Response-locked local field potentials in the theta and beta band (associated with conflict control and movement inhibition, respectively) were analyzed across subthalamic nucleus subregions and hemispheres relative to the motor response (ipsilateral/contralateral). In the presence of action conflict, the dorsal subthalamic nucleus, connected to cortical motor regions, exhibited larger theta power relative to the ventral subthalamic nucleus subregion which is linked to the limbic circuits (P<0.05). This evidence supports independent subregion function in conflict control. However, both subregions had relatively increased beta power for conflict trials compared to non-conflict in the hemisphere ipsilateral to the motor response. The conflict related beta modulation was not present in the contralateral hemisphere. This indicates the importance of the ipsilateral hemisphere in the inhibition of incorrect action impulses. Additionally, higher inter-trial beta power in the ventral subregion correlated with reduced accuracy on conflict trials, which we propose could serve as a biomarker for impaired task performance. The results of the study support the existence of a functional dissociation within subthalamic nucleus subregions, emphasizing the role of the dorsal subthalamic nucleus in modulating action conflict.

Subthalamic nucleus deep brain stimulation in the beta frequency range boosts cortical beta oscillations and slows down movement.

Recordings from Parkinson's disease (PD) patients typically show strong beta-band oscillations (13-35Hz), which can be modulated by deep brain stimulation (DBS). While high-frequency DBS (>100Hz) ameliorates motor symptoms and reduces beta activity in basal ganglia and motor cortex, the effects of low-frequency DBS (<30Hz) are less clear. Clarifying these effects is relevant for the debate about the role of beta oscillations in motor slowing, which might be causal or epiphenomenal. Here, we investigated how subthalamic nucleus (STN) beta-band DBS affects cortical beta oscillations and motor performance. We recorded the magnetoencephalogram of 14 PD patients (9 males) with DBS implants while on their usual medication. Following a baseline recording (DBS off), we applied bipolar DBS at beta frequencies (10, 16, 20, 26, and 30Hz) via the left electrode in a cyclic fashion, turning stimulation on (5s) and off (3s) repeatedly. For each frequency, cyclic stimulation was applied at rest and during right hand finger tapping. In the baseline recording, we observed a negative correlation between the strength of hemispheric beta power lateralization and the tap rate. Importantly, beta-band DBS accentuated the lateralization and reduced the tap rate proportionally. The change in lateralization was specific to the alpha/beta range (8-26Hz), outlasted stimulation and did not depend on the stimulation frequency, suggesting a remote induced response rather than entrainment. Our study demonstrates that cortical beta oscillations can be manipulated by STN beta-band DBS. Importantly, this manipulation appears to have consequences for motor performance, supporting a causal role of beta oscillations in motor control.Significance Statement The slowing of movement in Parkinson's disease is known to be related to pathologically enhanced neural oscillations in the beta range (13-35Hz). Whether these oscillations are the cause of motor slowing, however, remains debated. Here, we stimulated the brains of Parkinson patients in the beta range, using their implanted deep brain stimulator. Consistent with a causal effect, stimulation slowed down finger tapping. Additionally, the subcortical stimulation affected the hemispheric balance of beta oscillations in the remote motor cortices. The stronger the shift, the stronger was the slowing. These results suggest that subcortical beta-band stimulation causes motor slowing by boosting motor cortical beta oscillations, indicating that beta oscillations might indeed be "a bad boy in Parkinson's disease".

Neural dynamics of social verb processing: an MEG study.

Human vocabularies include specific words to communicate interpersonal behaviors, a core linguistic function mainly afforded by social verbs (SVs). This skill has been proposed to engage dedicated systems subserving social knowledge. Yet, neurocognitive evidence is scarce, and no study has examined spectro-temporal and spatial signatures of SV access. Here, we combined magnetoencephalography and time-resolved decoding methods to characterize the neural dynamics underpinning SVs, relative to nonsocial verbs (nSVs), via a lexical decision task. Time-frequency analysis revealed stronger beta (20 Hz) power decreases for SVs in right fronto-temporal sensors at early stages. Time-resolved decoding showed that beta oscillations significantly discriminated SVs and nSVs between 180 and 230 ms. Sources of this effect were traced to the right anterior superior temporal gyrus (a key hub underpinning social conceptual knowledge) as well as parietal, pre/motor and prefrontal cortices supporting nonverbal social cognition. Finally, representational similarity analyses showed that the observed fronto-temporal neural patterns were specifically predicted by verbs' socialness, as opposed to other psycholinguistic dimensions such as sensorimotor content, emotional valence, arousal, and concreteness. Overall, verbal conveyance of socialness seems to involve distinct neurolinguistic patterns, partly shared by more general sociocognitive and lexicosemantic processes.

Sex differences in olfactory behavior and neurophysiology in Long Evans rats.

In many species, olfactory abilities in females are more acute than those in males. Studies in humans show that women have lower olfactory thresholds and are better able to discriminate and identify odors than men. In mice, odorants elicit faster activation from a larger number of olfactory bulb glomeruli in females than in males. Our study explores sex differences in olfaction in Long Evans rats from a behavioral and electrophysiological perspective. Local field potentials (LFPs) in the olfactory bulb (OB) represent the coordinated activity of bulbar neurons. Olfactory gamma (65-120 Hz) and beta (15-30 Hz) oscillations have been functionally linked to odor perception. Spontaneous and odor-evoked OB LFPs were recorded from awake rats at the same time for 12 days. Odors used included urine of both sexes and monomolecular odorants characterized previously for correlation of volatility with behavior and OB oscillations. Sampling duration in a habituation context, baseline gamma and beta power, and odor-elicited beta and gamma power were analyzed. We find that females sample odorants for a shorter duration than males (just over 1-s difference). Although baseline gamma and beta power do not show significant differences between the two sexes, odor-elicited gamma and beta power in females is significantly lower than in males. Neither sampling duration nor beta and gamma power in females varied systematically with day of estrus. We further verify that variance of these behavioral and physiological measures is not different across sexes, adding to growing evidence that researchers need not be concerned about often-claimed additional variance in female subjects.NEW & NOTEWORTHY Olfaction plays a large role in evolutionary processes. However, we know little about sex differences in olfactory bulb neurophysiology, and many scientists believe that females are more variable because of estrus. We show that female rats sniff odors for shorter durations than males and have lower power in neural oscillations related to cognition. Estrus was not related to variance in any measures. Finally, males and females show equal variance on these behavioral and physiological processes.

Characterizing autogenous and reactive obsessions using theta and beta oscillations under inhibitory demands.

Obsessive-Compulsive Disorder (OCD) is a heterogenous mental health condition that causes significant impairment and is often associated with poor treatment outcomes. The aim of the current study was to examine the association between electroencephalographic (EEG) oscillatory power during inhibitory task performance and obsessive-compulsive symptoms (OCS). OCS was assessed using the well-established the Autogenous-Reactive Obsession (AO-RO) model as the main framework to address its heterogeneous clinical manifestations. The severity of AO and RO, as primary outcome measures, was indexed using the Revised Obsessive Intrusion Inventory (ROII). Cognitive- and behavioral inhibition (CI; BI) tasks were administered while EEG data were recorded from an analogue sample of 63 undergraduate students with OCS assessed along a dimensional spectrum. Oscillatory power was computed from frontal-central electrodes Fz and Cz for theta and beta frequency bands, using event-related spectral perturbations (ERSPs), which were entered into two hierarchical linear regression models to predict the severity of AO and RO, respectively, while controlling for covariates (i.e., sex, age, ethnicity, anxiety, depression, worry, and behavioral task performance). Theta power during CI (Theta-CI) was the only significant EEG predictor of AO severity, whereas beta power during BI (Beta-BI) was the only significant EEG predictor of RO severity. These results suggest that AO severity is primarily associated with an overactive neural correlate of cognitive control, whereas RO severity is primarily associated with an overactive neural correlate of behavioral cancellation. These results agree with previous literature suggesting overactive band power representing the dysfunction within OCD. Theta-CI and Beta-BI may serve as potential biomarkers differentially associated with AO and RO among undiagnosed individuals displaying varying levels of OCS, which warrants further investigation.

Individualized Spectral Features in First-episode and Drug-naïve Major Depressive Disorder: Insights from Periodic and Aperiodic EEG Analysis.

The detection of abnormal brain activity plays an important role in the early diagnosis and treatment of major depressive disorder (MDD). Recent studies have shown that the decomposition of the electroencephalography (EEG) spectrum into periodic and aperiodic components is useful for identifying the drivers of electrophysiologic abnormalities and avoiding individual differences. This study aimed to elucidate the pathologic changes in individualized periodic and aperiodic activities and their relationships with the symptoms of MDD. EEG data in the eyes-closed resting state were continuously recorded from 97 first-episode and drug-naïve patients with MDD and 90 healthy control (HC) participants. Both periodic oscillations and aperiodic components were obtained via the "fitting oscillations and one-over f" (FOOOF) algorithm and then used to compute individualized spectral features. MDD patients presented higher canonical alpha and beta band power but lower aperiodic-adjusted alpha and beta power. Furthermore, we found that alpha power was strongly correlated with the age of patients but not with disease symptoms. The aperiodic intercept was lower in the parietal‒occipital region and was positively correlated with the Hamilton Depression Rating Scale (HAMD) score after accounting for age and sex. In the asymmetry analysis, alpha activity appeared asymmetrical only in the HC group, whereas aperiodic activity was symmetrical in both groups. The findings of this study provide insights into the role of abnormal neural spiking activity and impaired neuroplasticity in MDD progression and suggest that the aperiodic intercept in resting-state EEG may be a potential biomarker of MDD.

How individuals evaluate the confidence of advice from advisors with high- and low-status: A behavioural and ERP study.

Although previous studies have shown that both advisors' social status and confidence level affect advisees' advice-taking behavior, it is currently unclear the mechanisms of their common actions. Here, using event-related potentials, we investigated how both advisors' social status and confidence level independently or jointly influence advice-taking behavior. Specifically, participants were asked to make choices in a dot-estimation task and then they would receive high- and low-confidence advice from advisors with high- and low-status. Behaviorally, an interaction effect between advisors' status and confidence was found, suggesting that individuals were more likely to take high-confidence (vs. low-confidence) advice whether it was from high-status or low-status advisors. However, such an effect of confidence was larger for high-status advisors rather than for low-status advisors. On the electrophysiological level, during the early stage of processing advice, an interaction effect between advisors' status and confidence was only observed on the theta power rather than the FRN component, suggesting that the larger theta power was observed for low-confidence (vs. high-confidence) advice from low-status advisors rather than high-status advisors. Besides, although the larger P3 and beta power were found for advice from high-status advisors (vs. low-status advisors) or advice with high-confidence (vs. low-confidence), no interaction effect between status and confidence was found. Taken together, our findings suggested that advisors' status and confidence might affect the multiple stages in different ways during processing advice.

Psychological and Physiological Effects of Creating a Seasonal Roadside Flowerscape on Police Officials

Background and objective: This study was conducted to find out the psychological and physiological effects of creating a seasonal roadside flowerscape on police officials.Methods: Police officials created and managed a spring flowerscape in the outer space of their workplace, through which the effects on stability, relaxation, and attention could be examined through brainwave changes. EEG measurements and surveys were conducted twice before and after the experiment on a total of 18 men and women with an average age of 37.1 ± 9.0.Results: As a result of investigating the changes in the EEG and ECG of police officials, it was found that not only stability, relaxation (RSMT: Ratio of (SMR-mid beta) to theta), and attention (RMT:Ratio of mid beta to theta, RMB: Relative mid beta power spectrum), but also brain activity (SEF 50%:Spectral edge frequency 50%) increased after creating a roadside flowerscape compared to before (&lt;i&gt;p&lt;/i&gt; &lt; .05). As a result of examining the difference between positive and negative affect of police officials before and after creating the roadside flowerscape, it was found that positive affect tended to increase after creating the flowerscape, while negative affect tended to decrease, indicating the positive effect of the flowerscape. Police officials’ preference for flower landscapes increased after creating the flowerscape (&lt;i&gt;p&lt;/i&gt; &lt; .05). As for the correlations between key variables and loyalty to the seasonal roadside flowerscape, loyalty, positive affect, and flowerscape preference of police officials showed positive correlations, while negative correlations were found with negative affect, showing statistical significance. The results of this study indicate that creating a seasonal roadside flowerscape inside and outside the workplace of police officials increases stability, relaxation, and attention of police officials while decreasing tension and stress.Conclusion: Therefore, the positive effects of creating a natural landscape using plants for police officials suffering from severe occupational trauma could improve their quality of life and relieve stress

EEG-Based Measurement for Detecting Distraction in Coal Mine Workers

In the high-attention-demanding environment of underground coal mines, distraction is a major cause of unsafe behavior and decreased safety performance. Research on the cognitive neural mechanisms and monitoring of distraction in miners is limited. This study used an electroencephalogram (EEG) to examine the correlation between distraction and brain activity in coal miners, aiming to provide an objective method for monitoring distraction in coal miners. Thirty participants completed a simulated hazard recognition task, using the Sustained Attention to Response Task (SART) and noise to induce distraction. Brain activity was recorded and labeled as focused or distracted based on the correctness of the hazard recognition task. EEG features were extracted and selected, and a Random Forest model for distraction identification was constructed based on the selected features. In the focused state, delta power in the temporal region and theta power in the frontal region increased significantly. In the distracted state, alpha power in the temporal and occipital regions and beta power in the occipital and parietal regions increased. The selected EEG features could be used to identify distraction with 84% accuracy. This method can objectively identify distraction in coal miners, highlighting the potential of using EEG for real-time distraction monitoring.

Comparative analysis of sleep physiology using qualitative and quantitative criteria for insomnia symptoms.

Despite decades of research, defining insomnia remains challenging due to its complex and variable nature. Various diagnostic systems emphasize the chronic nature of insomnia and its impact on daily functioning, relying heavily on patient self-reporting due to limitations in objective measures like polysomnography (PSG). Discrepancies between subjective experiences and objective PSG results highlight the need for more nuanced approaches, such as electroencephalogram (EEG) spectral analysis, which reveals distinct patterns of high-frequency activity in individuals with insomnia. This study explores EEG markers of insomnia by integrating subjective reports with objective physiological markers, specifically ORP (Odds-Ratio-Product) and spectral features, to address inconsistencies found in previous research and clinical settings. Qualitative and quantitative definitions of insomnia are contrasted to highlight differences in sleep architecture and EEG characteristics. The research aims to determine whether groups defined by weekly frequency and daily duration of symptoms have different distribution patterns and which physiological characteristics best distinguish insomnia patients from controls. Our findings suggest that ORP, as a dependent variable, captures the most significant differences in the independent variables across the model. Elevated beta power in insomnia patients indicates increased cortical arousal, supporting the perspective of insomnia as a hyperarousal disorder. Future research should focus on using ORP to enhance the understanding of sleep disturbances in insomnia. Comprehensive evaluation of insomnia requires integrating qualitative, quantitative, and neurophysiological data to fully understand its impact on sleep architecture and quality.

The Modulatory Effects of Transcranial Alternating Current Stimulation on Brain Oscillatory Patterns in the Beta Band in Healthy Older Adults.

Background: In the last few years, transcranial alternating current stimulation (tACS) has attracted attention as a promising approach to interact with ongoing oscillatory cortical activity and, consequently, to enhance cognitive and motor processes. While tACS findings are limited by high variability in young adults' responses, its effects on brain oscillations in older adults remain largely unexplored. In fact, the modulatory effects of tACS on cortical oscillations in healthy aging participants have not yet been investigated extensively, particularly during movement. This study aimed to examine the after-effects of 20 Hz and 70 Hz High-Definition tACS on beta oscillations both during rest and movement. Methods: We recorded resting state EEG signals and during a handgrip task in 15 healthy older participants. We applied 10 min of 20 Hz HD-tACS, 70 Hz HD-tACS or Sham stimulation for 10 min. We extracted resting-state beta power and movement-related beta desynchronization (MRBD) values to compare between stimulation frequencies and across time. Results: We found that 20 Hz HD-tACS induced a significant reduction in beta power for electrodes C3 and CP3, while 70 Hz did not have any significant effects. With regards to MRBD, 20 Hz HD-tACS led to more negative values, while 70 Hz HD-tACS resulted in more positive ones for electrodes C3 and FC3. Conclusions: These findings suggest that HD-tACS can modulate beta brain oscillations with frequency specificity. They also highlight the focal impact of HD-tACS, which elicits effects on the cortical region situated directly beneath the stimulation electrode.

Characterization of neuronal oscillations in the prelimbic cortex, nucleus accumbens and CA1 hippocampus during object retrieval task in rats predisposed to early life stress

BackgroundEarly life stress (ELS) during the stress hypo-responsive period (SHRP) alters the curiosity-like behavior later during adolescence. Previous studies have shown maternal separation (MS) stress-induced heightened curiosity and associated risk-taking behavior in the object retrieval task (ORT). However, the neural correlates of curiosity in adolescent rats predisposed to early life stress remain unexplored. Hence, the present study aimed to investigate the neural oscillatory patterns and network characteristics in the regions implicated in curiosity behavior, such as the Prelimbic cortex (PL), Nucleus Accumbens (NAc), and CA1 of the Hippocampus. The local field potentials data were analysed to understand the neural activity patterns in these areas during the risky zone crossing and object retrieval phase of the ORT in MS rats and compared with the normal control (NC) group.ResultsIn comparison to NC, MS rats showed a reduction in the theta power at 8–12 Hz, beta power at 12–20 Hz, and gamma power at 20–40 Hz range in the PL during risky zone crossing time. MS rats also showed reduced cross-correlation between PL-CA1 and reduced theta coherence between NAc-CA1 during risky zone crossing. During the object retrieval phase, the MS rats showed reduced peak cross-correlation between PL-CA1 and PL-NAc. Behaviourally, MS rats displayed an increased preference for the curiosity platform and retrieved more hidden objects, thus accounting for a higher curiosity index than controls.ConclusionIn summary, a reduced synchronization between the PL, NAc, and CA1 during the object retrieval task indicates how early MS stress during a critical developmental period impacts the limbic circuit connectivity. This corresponded with enhanced curiosity index in adolescent MS rats, predicting an altered intrinsic motivation and hence a higher susceptibility to substance use disorders during adolescence.

Aperiodic and oscillatory systems underpinning human domain-general cognition

Domain-general cognitive systems are essential for adaptive human behaviour, supporting various cognitive tasks through flexible neural mechanisms. While fMRI studies link frontoparietal network activation to increasing demands across various tasks, the electrophysiological mechanisms underlying this domain-general response to demand remain unclear. Here, we used MEG/EEG, and separated the aperiodic and oscillatory components of the signals to examine their roles in domain-general cognition across three cognitive tasks using multivariate analysis. We found that both aperiodic (broadband power, slope, and intercept) and oscillatory (theta, alpha, and beta power) components coded task demand and content across all subtasks. Aperiodic broadband power in particular strongly coded task demand, in a manner that generalised across all subtasks. Source estimation suggested that increasing cognitive demand decreased aperiodic broadband power across the brain, with the strongest modulations overlapping with the frontoparietal network. In contrast, oscillatory activity showed more localised patterns of modulation, primarily in frontal or occipital regions. These results provide insights into the electrophysiological underpinnings of human domain-general cognition, highlighting the critical role of aperiodic broadband power.

Emotion regulation strategies explain associations of theta and Beta with positive affect.

Maladaptive emotion regulation (ER) strategies are a transdiagnostic construct in psychopathology. ER depends on cognitive control, so brain activity associated with cognitive control, such as frontal theta and beta, may be a factor in ER. This study investigated the association of theta and beta power with positive affect and whether emotion regulation strategies explain this association. One hundred and twenty-one undergraduate students (mean age = 20.74, SD = 5.29; 73% women) completed self-report questionnaires, including the Emotion Regulation Questionnaire and the Positive and Negative Affect Schedule. Spectral analysis was performed on resting state frontal electroencephalogram activity that was collected for eight 1-min periods of alternating open and closed eyes. Relative beta and theta band power were extracted relative to global field power at frontal channels. Regression analysis revealed that positive affect is significantly predicted by theta power (β = 0.24, p = .007) and beta power (β = -0.33, p < .0001). There was an indirect effect of beta power on positive affect via reappraisal, but not suppression. Additionally, theta power significantly predicted suppression, but no indirect effect was observed between theta power and positive affect. These findings are consistent with a prior study reporting a positive and negative relationship between theta and beta power, respectively, and positive affect induction. This study elucidates how modulation of theta and beta bands link to ER strategies.

Surfing beta burst waveforms to improve motor imagery-based BCI

Abstract Our understanding of motor-related, macroscale brain processes has been significantly shaped by the description of the event-related desynchronization (ERD) and synchronization (ERS) phenomena in the mu and beta frequency bands prior to, during, and following movement. The demonstration of reproducible, spatially- and band-limited signal power changes has, consequently, attracted the interest of non-invasive brain-computer interface (BCI) research for a long time. BCIs often rely on motor imagery (MI) experimental paradigms that are expected to generate brain signal modulations analogous to movement-related ERD and ERS. However, a number of recent neuroscience studies has questioned the nature of these phenomena. Beta band activity has been shown to occur, on a single-trial level, in short, transient, and heterogeneous events termed bursts rather than sustained oscillations. In a previous study, we established that an analysis of hand MI binary classification tasks based on beta bursts can be superior to beta power in terms of classification score. In this article, we elaborate on this idea, proposing a signal processing algorithm that is comparable to- and compatible with state-of-the-art techniques. Our pipeline filters brain recordings by convolving them with kernels extracted from beta bursts and then applies spatial filtering before classification. This data-driven filtering allowed for a simple and efficient analysis of signals from multiple sensors, thus being suitable for online applications. By adopting a time-resolved decoding approach, we explored MI dynamics and showed the specificity of the new classification features. In accordance with previous results, beta bursts improved classification performance compared to beta band power, while often increasing information transfer rate compared to state-of-the-art approaches.

Pallidal and motor cortical interactions determine gait initiation dynamics in Parkinson's disease.

Gait initiation is a fundamental human task, requiring one or more anticipatory postural adjustments (APA) prior to stepping. Deviations in amplitude and timing of APAs exist in Parkinson's disease (PD), causing dysfunctional postural control which increases the risk of falls. The motor cortex and basal ganglia have been implicated in the regulation of postural control, however, their dynamics during gait initiation, relationship to APA metrics, and response to pharmacotherapy such as levodopa are unknown. To address these questions, we streamed electrocorticography (ECoG) potentials from the premotor and primary motor cortices, as well as local field potentials (LFPs) from the globus pallidus in five people with PD exhibiting gait and balance dysfunction during a cued gait initiation task. Amplitude and timing of APA were evaluated with force plates and synchronized to the neural data. Subjects performed gait initiation trials under ON and LOW levodopa conditions to assess effects of medication on APA metrics and underlying neural dynamics. All subjects demonstrated pallidal and cortical oscillatory changes during different phases of gait initiation. Grouped analysis revealed that from quiet standing to the first foot step, pallidal beta power showed stepwise decrease and broadband gamma power increases, whereas cortical potentials showed low frequency (theta, alpha, beta) power decrease during gait initiation, regardless of medication state. The pallidum and motor cortices also became increasingly coherent during gait initiation compared to quiet standing prior to APA onset. Using linear mixed models, we found that while pallidal gamma powers are predictive of APA scaling, pallidal-cortical coherence (theta, alpha, beta) and premotor-M1 gamma coherence are predictive of APA timing. Our study is the first detailed characterization of basal-ganglia cortical circuit dynamics during human gait initiation. We identified significant pallidal motor cortical power and coherence changes that underlie the amplitude and timing of APA which appear to be independent of medication states of the study subjects. Our results provide evidence for a model where synchronized premotor and motor cortical activities transiently couple with the globus pallidus to regulate the timing of postural responses, and local pallidal activity regulate the amplitude of postural changes during gait initiation. It suggests that abnormal pallidal outflow and synchronization between the pallidum and motor cortices may be a pathophysiological mechanism underlying disordered postural response in Parkinson's disease.

Discovering EEG biomarkers of Lennox-Gastaut syndrome through unsupervised time-frequency analysis.

The discovery and validation of electroencephalography (EEG) biomarkers often rely on visual identification of waveforms. However, bias toward visually striking events restricts the search space for new biomarkers, and low interrater reliability can limit rigorous validation. We present a data-driven approach to biomarker discovery called scalp EEG Pattern Identification and Categorization (s-EPIC), which enables automated, unsupervised identification of EEG waveforms. S-EPIC is validated on Lennox-Gastaut syndrome (LGS), an epilepsy that is difficult to diagnose and assess due to its variable presentation and insidious evolution of symptoms. We retrospectively collected 10-min scalp EEG clips during non-rapid eye movement (NREM) sleep from 20 subjects with LGS and 20 approximately age-matched healthy controls. For s-EPIC, EEG events of interest (EOIs) were detected in all subjects using time-frequency analysis. The 11 705 EOIs were characterized based on 11 features and were collectively grouped using both k-means clustering and feature categorization. To provide clinical context, 1350 EOIs were visually reviewed and classified by three epileptologists. s-EPIC identified four clusters as candidate biomarkers of LGS, each having significantly more LGS EOIs than control EOIs. Two clusters contained EOIs resembling known LGS biomarkers such as interictal epileptiform discharges and generalized paroxysmal fast activity. The other two LGS-associated EEG clusters contained short bursts of power in beta and gamma frequency bands that were primarily unrecognized by epileptologists. This approach also uncovered significant differences in sleep spindles between LGS and control cohorts. s-EPIC provides a quantitative approach to waveform identification that could be broadly applied to EEG from both healthy subjects and those with suspected pathology. s-EPIC can objectively identify and characterize relevant EEG waveforms without visual review or assumptions about the waveform's morphology and could therefore be a powerful tool for the discovery and refinement of EEG biomarkers.

P102 Can EEG spectral analysis distinguish children with narcolepsy from those with idiopathic hypersomnolence and subjective sleepiness?

Abstract Background EEG spectral analysis provides a measurement of sleep micro-architecture and is a more sensitive measure of sleep disruption than conventional sleep macro-architecture. We aimed to examine the use of this technique in distinguishing children with narcolepsy and idiopathic hypersomnolence (IH) from subjectively sleepy children with a negative multiple sleep latency test. Methods Relative power, the absolute power in each frequency band divided by the total absolute power, was calculated for delta power (0.5 Hz–3.9 Hz), theta power (4 Hz–7.9 Hz), alpha power (8 Hz–11.9 Hz), sigma power (12 Hz–13.9 Hz) and beta power (14 Hz–30 Hz). A mean value for each frequency was calculated for each 30 s epoch then averaged for each sleep stage within each child. Results 28 children with Narcolepsy, 11 with IH and 26 with subjective sleepiness were included and individually matched for age and sex with a control child. In N2, Theta power was higher in the Narcolepsy group (median 13.5%, IQR 11.2-15.5) compared to the subjectively sleepy group (11.1%, 9.0-12.8, p&amp;lt;0.05). During N3, Delta power was higher in the subjectively sleepy group (93.3%, 91.9-93.9 vs 83.3%, 65.8-93.9, p&amp;lt;0.001), and Theta power (4.8%, 4.2-6.3 vs 12.6% 4.8-22.5, p&amp;lt;0.001), Alpha power (0.9%, 0.7-1.3 vs 2.8% 0.9-5.8, p&amp;lt;0.001), and Beta power were lower (0.3% 0.3 -0.4 vs 1.1% 0.3-4.2, p&amp;lt;0.05) compared to their controls. Conclusions Differences in sleep micro-architecture could not distinguish between children with narcolepsy and IH but may be able to distinguish children with narcolepsy from subjectively sleepy children.

Different oscillatory mechanisms of dementia-related diseases with cognitive impairment in closed-eye state

The escalating global trend of aging has intensified the focus on health concerns prevalent among the elderly. Notably, Dementia related diseases, including Alzheimer's disease (AD) and frontotemporal dementia (FTD), significantly impair the quality of life for both affected seniors and their caregivers. However, the underlying neural mechanisms of these diseases remain incompletely understood, especially in terms of neural oscillations. In this study, we leveraged an open dataset containing 36 CE, 23 FTD, and 29 healthy controls (HC) to investigate these mechanisms. We accurately and clearly identified three stable oscillation targets (theta, ∼5 Hz, alpha, ∼10 Hz, and beta, ∼18 Hz) that facilitate differentiation between AD, FTD, and HC both statistically and through classification using machine learning algorithms. Overall, the differences between AD and HC were the most pronounced, with FTD exhibiting intermediate characteristics. The differences in the theta and alpha bands showed a global pattern, whereas the differences in the beta band were localized to the central-temporal region. Moreover, our analysis revealed that the relative theta power was significantly and negatively correlated with the Mini Mental State Examination (MMSE) scores, while the relative alpha and beta power showed a significant positive correlation. This study is the first to pinpoint multiple robust and effective neural oscillation targets to distinguish AD, offering a simple and convenient method that holds promise for future applications in the early screening of large-scale dementia-related diseases.

Exploring the cognitive underpinnings of early hominin stone tool use through an experimental EEG approach.

Technological innovation has been crucial in the evolution of our lineage, with tool use and production linked to complex cognitive processes. While previous research has examined the cognitive demands of early stone toolmaking, the neurocognitive aspects of early hominin tool use remain largely underexplored. This study relies on electroencephalography to investigate brain activation patterns associated with two distinct early hominin tool-using behaviors: forceful hammerstone percussion, practiced by both humans and non-human primates and linked to the earliest proposed stone tool industries, and precise flake cutting, an exclusive hominin behavior typically associated with the Oldowan. Our results show increased engagement of the frontoparietal regions during both tasks. Furthermore, we observed significantly increased beta power in the frontal and centroparietal areas when manipulating a cutting flake compared to a hammerstone, and increased beta activity over contralateral frontal areas during the aiming (planning) stage of the tool-using process. This original empirical evidence suggests that certain fundamental brain changes during early hominin evolution may be linked to precise stone tool use. These results offer new insights into the complex interplay between technology and human brain evolution and encourage further research on the neurocognitive underpinnings of hominin tool use.