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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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 International Brain Research Organization (IBRO). Published by Elsevier Inc. All rights reserved.)

From 1990-2019, the burden of neurological disorders varied considerably across countries and regions. Psychiatric disorders, often emerging in early to mid-adulthood, are linked to late-life neurodegenerative diseases like Alzheimer's disease and Parkinson's disease. Individuals with conditions such as Major Depressive Disorder, Anxiety Disorder, Schizophrenia, and Bipolar Disorder face up to four times higher risk of developing neurodegenerative disorders. Contrarily, 65 % of those with neurodegenerative conditions experience severe psychiatric symptoms during their illness. Further, the limitation of medical resources continues to make this burden a significant global and local challenge. Therefore, brainwave entrainment provides therapeutic avenues for improving the symptoms of diseases. Brainwaves are rhythmic oscillations produced either spontaneously or in response to stimuli. Key brainwave patterns include gamma, beta, alpha, theta, and delta waves, yet the underlying physiological mechanisms and the brain's ability to shift between these dynamic states remain areas for further exploration. In neurological disorders, brainwaves are often disrupted, a phenomenon termed "oscillopathy". However, distinguishing these impaired oscillations from the natural variability in brainwave activity across different regions and functional states poses significant challenges. Brainwave-mediated therapeutics represents a promising research field aimed at correcting dysfunctional oscillations. Herein, we discuss a range of non-invasive techniques such as non-invasive brain stimulation (NIBS), neurologic music therapy (NMT), gamma stimulation, and somatosensory interventions using light, sound, and visual stimuli. These approaches, with their minimal side effects and cost-effectiveness, offer potential therapeutic benefits. When integrated, they may not only help in delaying disease progression but also contribute to the development of innovative medical devices for neurological care., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial and conflict of interests in this manuscript., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., Competing Interests: Conflict of interest None of the authors have potential conflicts of interest to be disclosed., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Changes in brain oscillatory activity are commonly used as biomarkers both in cognitive neuroscience and in neuropsychiatric conditions. However, little is known about how its profile changes across maturation. Here we use regression models to characterize magnetoencephalography power changes within classical frequency bands in a sample of 792 healthy participants, covering the range 13 to 80 years old. Our findings unveil complex, non-linear power trajectories that defy the traditional linear paradigm, with notable cortical region variations. Interestingly, slow wave activity increases correlate with improved cognitive performance throughout life and larger gray matter volume in the elderly. Conversely, fast wave activity diminishes in adulthood. Elevated low-frequency activity during aging, traditionally seen as compensatory, may also signify neural deterioration. This dual interpretation, highlighted by our study, reveals the intricate dynamics between brain oscillations, cognitive performance, and aging. It advances our understanding of neurodevelopment and aging by emphasizing the regional specificity and complexity of brain rhythm changes, with implications for cognitive and structural integrity., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Ictal stereo-encephalography (sEEG) biomarkers for seizure onset zone (SOZ) localization can be classified depending on whether they target abnormalities in signal power or functional connectivity between signals, and they may depend on the frequency band and time window at which they are estimated., New Method: This work aimed to compare and optimize the performance of a power and a connectivity-based biomarker to identify SOZ contacts from ictal sEEG recordings. To do so, we used a previously introduced power-based measure, the normalized mean activation (nMA), which quantifies the ictal average power activation. Similarly, we defined the normalized mean strength (nMS), to quantify the ictal mean functional connectivity of every contact with the rest. The optimal frequency bands and time windows were selected based on optimizing AUC and F2-score., Results: The analysis was performed on a dataset of 67 seizures from 10 patients with pharmacoresistant temporal lobe epilepsy. Our results suggest that the power-based biomarker generally performs better for the detection of SOZ than the connectivity-based one. However, an equivalent performance level can be achieved when both biomarkers are independently optimized. Optimal performance was achieved in the beta and lower-gamma range for the power biomarker and in the lower- and higher-gamma range for connectivity, both using a 20 or 30 s period after seizure onset., Conclusions: The results of this study highlight the importance of this optimization step over frequency and time windows when comparing different SOZ discrimination biomarkers. This information should be considered when training SOZ classifiers on retrospective patients' data for clinical applications., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Neural oscillations are electrophysiological indicators of synchronous neuronal activity in the brain. Recent work suggests aberrant patterns of neuronal activity in patients with poststroke aphasia. Yet, there is a lack of systematic explorations of neural oscillations in poststroke aphasia. Investigating changes in the dynamics of neuronal activity after stroke may be helpful to identify neural markers of aphasia and language recovery and increase the current understanding of successful language rehabilitation. This review summarizes research on neural oscillations in poststroke aphasia and evaluates their potential as biomarkers for specific linguistic processes. We searched the literature through PubMed, Web of Science, and EBSCO, and selected 31 studies that met the inclusion criteria. Our analyses focused on neural oscillation activity in each frequency band, brain connectivity, and therapy-induced changes during language recovery. Our review highlights potential neurophysiological markers; however, the literature remains confounded, casting doubt on the reliability of these findings. Future research must address these confounds to confirm the robustness of cross-study findings on neural oscillations in poststroke aphasia., (© 2024 Society for Psychophysiological Research.)

Abnormal patterns of brain connectivity characterize epilepsy. However, little is known about these patterns during the stages preceding a seizure induced by pentylenetetrazol (PTZ). To investigate brain connectivity in male Wistar rats during the preictal phase of PTZ-induced seizures (60 mg/kg), we recorded local field potentials in the primary motor (M1) cortex, the ventral anterior (VA) nucleus of the thalamus, the hippocampal CA1 area, and the dentate gyrus (DG) during the baseline period and after PTZ administration. While there were no changes in power density between the baseline and preictal periods, we observed an increase in directional functional connectivity in theta from the hippocampal formation to M1 and VA, as well as in middle gamma from DG to CA1 and from CA1 to M1, and also in slow gamma from M1 to CA1. These findings are supported by increased phase coherence between DG-M1 in theta and CA1-M1 in middle gamma, as well as enhanced phase-amplitude coupling of delta-middle gamma in M1 and delta-fast gamma in CA1. Interestingly, we also noted a slight decrease in phase synchrony between CA1 and VA in slow gamma. Together, these results demonstrate increased functional connectivity between brain regions during the PTZ-induced preictal period, with this increase being particularly driven by the hippocampal formation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Sleep is known to drive the consolidation of motor memories. During nonrapid eye movement (NREM) sleep, the close temporal proximity between slow oscillations (SOs) and spindles ("nesting" of SO-spindles) is known to be essential for consolidation, likely because it is closely associated with the reactivation of awake task activity. Interestingly, recent work has found that spindles can occur in temporal clusters or "trains." However, it remains unclear how spindle trains are related to the nesting phenomenon. Here, we hypothesized that spindle trains are more likely when SOs co-occur in the prefrontal and motor cortex. We conducted simultaneous neural recordings in the medial prefrontal cortex (mPFC) and primary motor cortex (M1) of male rats training on the reach-to-grasp motor task. We found that intracortically recorded M1 spindles are organized into distinct temporal clusters. Notably, the occurrence of temporally precise SOs between mPFC and M1 was a strong predictor of spindle trains. Moreover, reactivation of awake task patterns is much more persistent during spindle trains in comparison with that during isolated spindles. Together, our work suggests that the precise coupling of SOs across mPFC and M1 may be a potential driver of spindle trains and persistent reactivation of motor memory during NREM sleep., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 the authors.)

The paper introduces a parametric resonance model for characterizing some features of the brain's electrical activity. This activity is assumed to be a fundamental aspect of brain functionality underpinning functions from basic sensory processing to complex cognitive operations such as memory, reasoning, and emotion. A pivotal element of the proposed parametric model is neuron synchronization which is crucial for generating detectable brain waves. The analysis of the frequency content of brain waves, categorized as delta (0÷4 Hz), theta (4÷7 Hz), alpha (8÷12 Hz), beta (13÷30 Hz), and gamma (30÷100 Hz) reveals, notably, that the mean frequency of each of these brain wave classes is, in sequence, approximately the double of that of the previous one. Based on this observation, the proposed parametric resonance model suggests a cascade of amplification effects. Following the proposed model, in the transition from wakefulness to sleep, the brain wave bands are energized at double frequency by higher frequency neighboring bands; on the contrary, in the sleep to awake transition, brain waves are energized at a half frequency by their lower frequency neighbor waves. Finally, the trend of increasing amplitude values from higher to lower frequencies lends empirical support to the parametric resonant brain model validity., (© 2024. The Author(s).)

Despite effective antiretroviral therapy, cognitive impairment remains prevalent among people with HIV (PWH) and decrements in executive function are particularly prominent. One component of executive function is cognitive flexibility, which integrates a variety of executive functions to dynamically adapt one's behavior in response to changing contextual demands. Though substantial work has illuminated HIV-related aberrations in brain function, it remains unclear how the neural oscillatory dynamics serving cognitive flexibility are affected by HIV-related alterations in neural functioning. Herein, 149 participants (PWH: 74; seronegative controls: 75) between the ages of 29-76 years completed a perceptual feature matching task that probes cognitive flexibility during high-density magnetoencephalography (MEG). Neural responses were decomposed into the time-frequency domain and significant oscillatory responses in the theta (4-8 Hz), alpha (10-16 Hz), and gamma (74-98 Hz) spectral windows were imaged using a beamforming approach. Whole-brain voxel-wise comparisons were then conducted on these dynamic functional maps to identify HIV-related differences in the neural oscillatory dynamics supporting cognitive flexibility. Our findings indicated group differences in alpha oscillatory activity in the cingulo-opercular cortices, and differences in gamma activity were found in the cerebellum. Across all participants, alpha and gamma activity in these regions were associated with performance on the cognitive flexibility task. Further, PWH who had been treated with antiretroviral therapy for a longer duration and those with higher current CD4 counts had alpha responses that more closely resembled those of seronegative controls, suggesting that optimal clinical management of HIV infection is associated with preserved neural dynamics supporting cognitive flexibility., Competing Interests: Declaration of competing interest SHB reports scientific advisory to Gilead Sciences and research grants to her institution from ViiV Healthcare and Janssen. All other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

This research investigated the modulation of acupuncture at Quchi (LI11) on the brain activities in healthy individuals. Sub-bands power and EEG microstate analysis were carried out at pre-acupuncture, acupuncture, needle retaining and post-acupuncture periods in both the acupuncture group (n = 16) and control group (n = 18). Four microstate classes (A-D) were derived from the clustering procedure. Regression analysis was conducted, together with a two-way repeated measures ANOVA, which was then followed by Bonferroni correction. In the acupuncture group, we found the beta power during the acupuncture periods was significantly reduced. The channel-by-channel analysis revealed that acupuncture at LI11 mainly altered the power of delta, theta, and alpha waves in specific brain regions. The delta power increased predominantly in parietal, occipital, and central lobes, while theta and alpha power decreased predominantly in temporal, frontal, parietal, and occipital lobes. During the acupuncture period, participants in the acupuncture group showed a significant increase in both duration and contribution of microstate A, as well as the bidirectional transition probabilities A and B/D. Microstate analysis showed that acupuncture at LI11 significantly enhances the activity of microstate A and potentially strengthens the functional connectivity between the auditory network and either the visual network or the dorsal attention network. These correlational results indicate that acupuncture at LI11 mainly affects activities of the frontal, temporal, parietal, and occipital lobes. These findings highlight the potential of microstate as neuroimaging evidence and a specific index for elucidating the neuromodulatory effects of acupuncture at LI11., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Human mobility requires neurocognitive inputs to safely navigate the environment. Previous research has examined neural processes that underly walking using mobile neuroimaging technologies, yet few studies have incorporated true real-world methods without a specific task imposed on participants (e.g., dual-task, motor demands). The present study included 40 young adults (M = 22.60, SD = 2.63, 24 female) and utilized mobile electroencephalography (EEG) to examine and compare theta, alpha, and beta frequency band power (μV 2 ) during sitting and walking in laboratory and real-world environments. EEG data was recorded using the Muse S brain sensing headband, a portable system equipped with four electrodes (two frontal, two temporal) and one reference sensor. Qualitative data detailing the thoughts of each participant were collected after each condition. For the quantitative data, a 2 × 2 repeated measures ANOVA with within subject factors of environment and mobility was conducted with full participant datasets (n = 17, M = 22.59, SD = 2.97, 10 female). Thematic analysis was performed on the qualitative data (n = 40). Our findings support that mobility and environment may modulate neural activity, as we observed increased brain activation for walking compared to sitting, and for real-world walking compared to laboratory walking. We identified five qualitative themes across the four conditions 1) physical sensations and bodily awareness, 2) responsibilities and planning, 3) environmental awareness, 4) mobility, and 5) spotlight effect. Our study highlights the importance and potential for real-world methods to supplement standard research practices to increase the ecological validity of studies conducted in the fields of neuroscience and kinesiology., (Crown Copyright © 2024. Published by Elsevier Ltd. All rights reserved.)

Objective. Serving as a channel for communication with locked-in patients or control of prostheses, sensorimotor brain-computer interfaces (BCIs) decode imaginary movements from the recorded activity of the user's brain. However, many individuals remain unable to control the BCI, and the underlying mechanisms are unclear. The user's BCI performance was previously shown to correlate with the resting-state signal-to-noise ratio (SNR) of the mu rhythm and the phase synchronization (PS) of the mu rhythm between sensorimotor areas. Yet, these predictors of performance were primarily evaluated in a single BCI session, while the longitudinal aspect remains rather uninvestigated. In addition, different analysis pipelines were used to estimate PS in source space, potentially hindering the reproducibility of the results. Approach. To systematically address these issues, we performed an extensive validation of the relationship between pre-stimulus SNR, PS, and session-wise BCI performance using a publicly available dataset of 62 human participants performing up to 11 sessions of BCI training. We performed the analysis in sensor space using the surface Laplacian and in source space by combining 24 processing pipelines in a multiverse analysis. This way, we could investigate how robust the observed effects were to the selection of the pipeline. Main results. Our results show that SNR had both between- and within-subject effects on BCI performance for the majority of the pipelines. In contrast, the effect of PS on BCI performance was less robust to the selection of the pipeline and became non-significant after controlling for SNR. Significance. Taken together, our results demonstrate that changes in neuronal connectivity within the sensorimotor system are not critical for learning to control a BCI, and interventions that increase the SNR of the mu rhythm might lead to improvements in the user's BCI performance., (Creative Commons Attribution license.)

When exposed to rhythmic stimulation, the human brain displays rhythmic activity across sensory modalities and regions. Given the ubiquity of this phenomenon, how sensory rhythms are transformed into neural rhythms remains surprisingly inconclusive. An influential model posits that endogenous oscillations entrain to external rhythms, thereby encoding environmental dynamics and shaping perception. However, research on neural entrainment faces multiple challenges, from ambiguous definitions to methodological difficulties when endogenous oscillations need to be identified and disentangled from other stimulus-related mechanisms that can lead to similar phase-locked responses. Yet, recent years have seen novel approaches to overcome these challenges, including computational modeling, insights from dynamical systems theory, sophisticated stimulus designs, and study of neuropsychological impairments. This review outlines key challenges in neural entrainment research, delineates state-of-the-art approaches, and integrates findings from human and animal neurophysiology to provide a broad perspective on the usefulness, validity, and constraints of oscillatory models in brain-environment interaction., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 the authors.)

Mu rhythm (∼8-12 Hz) in the somatosensory cortex has traditionally been linked with doing and seeing motor activities. Here, we aimed to learn how the medium (physical or screened) in which motor actions are seen could impact on that specific brain rhythm. To do so, we presented to 40 participants the very same narrative content both in a one-shot movie with no cuts and in a real theatrical performance. We recorded subjects' brain activities with electroencephalographic (EEG) procedures, and analyzed Mu rhythm present in left (C3) and right (C4) somatosensory areas in relation to the 24 motor activities included in each visual stimulus (screen vs. reality) (24 motor and grasping actions x 40 participants x 2 conditions = 1920 trials). We found lower Mu spectral power in the somatosensory area after the onset of the motor actions in real performance than on-screened content, more pronounced in the left hemisphere. In our results, the sensorimotor Mu-ERD (event-related desynchronization) was stronger during the real-world observation compared to screen observation. This could be relevant in research areas where the somatosensory cortex is important, such as online learning, virtual reality, or brain-computer interfaces., (© 2024. The Author(s).)

Executive control of movement enables inhibiting impulsive responses critical for successful navigation of the environment. Circuits mediating stop commands involve prefrontal and basal ganglia structures with fMRI evidence demonstrating increased activity during response inhibition in the dorsolateral prefrontal cortex (dlPFC)-often ascribed to maintaining task attentional demands. Using direct intraoperative cortical recordings in male and female human subjects, we investigated oscillatory dynamics along the rostral-caudal axis of dlPFC during a modified Go/No-go task, probing components of both proactive and reactive motor control. We assessed whether cognitive control is topographically organized along this axis and observed that low-frequency power increased prominently in mid-rostral dlPFC when inhibiting and delaying responses. These findings provide evidence for a key role for mid-rostral dlPFC low-frequency oscillations in sculpting motor control., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 the authors.)

Objective: Interictal biomarkers are critical for identifying the epileptogenic focus. However, spikes and ripples lack specificity while fast ripples lack sensitivity. These biomarkers propagate from more epileptogenic onset to areas of spread. The pathophysiological mechanism of these propagations is elusive. Here, we examine zones where spikes and high frequency oscillations co-occur (SHFO), the spatiotemporal propagations of spikes, ripples, and fast ripples, and evaluate the spike-ripple onset overlap (SRO) as an epilepsy biomarker., Methods: We retrospectively analyzed intracranial EEG data from 41 patients with drug-resistant epilepsy. We mapped propagations of spikes, ripples, and fast ripples, and identified their onset and spread zones, as well as SHFO and SRO. We then estimated the SRO prognostic value in predicting surgical outcome and compared it to onset and spread zones of spike, ripple, and fast ripple propagations, and SHFO., Results: We detected spikes and ripples in all patients and fast ripples in 12 patients (29%). We observed spike and ripple propagations in 40 (98%) patients. Spike and ripple onsets overlapped in 35 (85%) patients. In good outcome patients, SRO showed higher specificity and precision (p < 0.05) in predicting resection compared to onset and zones of spikes, ripples, and SHFO. Only SRO resection predicted outcome (p = 0.01) with positive and negative predictive values of 82% and 57%, respectively., Interpretation: SRO is a specific and precise biomarker of the epileptogenic zone whose removal predicts outcome. SRO is present in most patients with drug-resistant epilepsy. Such a biomarker may reduce prolonged intracranial monitoring and improve outcome., (© 2024 The Author(s). Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

The capabilities of transformer networks such as ChatGPT and other large language models (LLMs) have captured the world's attention. The crucial computational mechanism underlying their performance relies on transforming a complete input sequence - for example, all the words in a sentence - into a long 'encoding vector' that allows transformers to learn long-range temporal dependencies in naturalistic sequences. Specifically, 'self-attention' applied to this encoding vector enhances temporal context in transformers by computing associations between pairs of words in the input sequence. We suggest that waves of neural activity traveling across single cortical areas, or multiple regions on the whole-brain scale, could implement a similar encoding principle. By encapsulating recent input history into a single spatial pattern at each moment in time, cortical waves may enable a temporal context to be extracted from sequences of sensory inputs, the same computational principle as that used in transformers., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The relationship between external feedback and cognitive and neurophysiological performance has been extensively investigated in social neuroscience. However, few studies have considered the role of positive and negative external social feedback on electroencephalographic (EEG) and moderate stress response. Twenty-six healthy adults underwent a moderately stressful job interview consisting of a modified version of the Trier Social Stress Test. After each preparation, feedback was provided by an external committee, ranging from positive to negative with increasing impact on subjects. Stress response was measured by analysing response times (RTs) during the speech phase, while cognitive performance was assessed using a Stroop-like task before and after the test. Results indicate that RTs used to deliver the final speeches with negative feedback were significantly lower compared with those used for the initial speech with positive feedback. Moreover, a generalized improvement in Stroop-like task performance was observed in the post-SST compared with the pre-SST. Consistent with behavioural results, EEG data indicated greater delta, theta, and alpha band responses in right prefrontal and left central areas, and for delta and theta bands, also in parietal areas in response to positive feedback compared with aversive-neutral feedback, highlighting greater cognitive effort required by the former. Conversely, an increase in these bands in right and left temporal and left occipital areas was observed following negative and aversive feedback, indicative of an adaptive response to stress and emotion-regulatory processes. These findings suggest that negative social feedback in moderately stressful and noncritical conditions could contribute to improving individual cognitive performance., (© 2024 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Cell replacement therapies using medial ganglionic eminence (MGE)-derived GABAergic precursors reduce seizures by restoring inhibition in animal models of epilepsy. However, how MGE-derived cells affect abnormal neuronal networks and consequently brain oscillations to reduce ictogenesis is still under investigation. We performed quantitative analysis of pre-ictal local field potentials (LFP) of cortical and hippocampal CA1 areas recorded in vivo in the pilocarpine rat model of epilepsy, with or without intrahippocampal MGE-precursor grafts (PILO and PILO+MGE groups, respectively). The PILO+MGE animals had a significant reduction in the number of seizures. The quantitative analysis of pre-ictal LFP showed decreased power of cortical and hippocampal delta, theta and beta oscillations from the 5 min. interictal baseline to the 20 s. pre-ictal period in both groups. However, PILO+MGE animals had higher power of slow and fast oscillations in the cortex and lower power of slow and fast oscillations in the hippocampus compared to the PILO group. Additionally, PILO+MGE animals exhibited decreased cortico-hippocampal synchrony for theta and gamma oscillations at seizure onset and lower hippocampal CA1 synchrony between delta and theta with slow gamma oscillations compared to PILO animals. These findings suggest that MGE-derived cell integration into the abnormally rewired network may help control ictogenesis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: Coupling of sleep spindles with cortical slow waves and hippocampus sharp-waves ripples is crucial for sleep-related memory consolidation. Recent literature evidenced that nasal respiration modulates neural activity in large-scale brain networks. In rodents, this respiratory drive strongly varies according to vigilance states. Whether sleep oscillations are also respiration-modulated in humans remains open. In this work, we investigated the influence of breathing on sleep spindles during non-rapid-eye-movement sleep in humans., Methods: Full night polysomnography of twenty healthy participants were analysed. Spindles and slow waves were automatically detected during N2 and N3 stages. Spindle-related sigma power as well as spindle and slow wave events were analysed according to the respiratory phase., Results: We found a significant coupling between both slow and fast spindles and the respiration cycle, with enhanced sigma activity and occurrence probability of spindles during the middle part of the expiration phase. A different coupling was observed for slow waves negative peaks which were rather distributed around the two respiration phase transitions., Conclusion: Our findings suggest that breathing cycle influences the dynamics of brain activity during non-rapid-eye-movement sleep., Significance: This coupling may enable sleep spindles to synchronize with other sleep oscillations and facilitate information transfer between distributed brain networks., (Copyright © 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Whether high-frequency phase-locked oscillations facilitate integration ('binding') of information across widespread cortical areas is controversial. Here we show with intracranial electroencephalography that cortico-cortical co-ripples (~100-ms-long ~90 Hz oscillations) increase during reading and semantic decisions, at the times and co-locations when and where binding should occur. Fusiform wordform areas co-ripple with virtually all language areas, maximally from 200 to 400 ms post-word-onset. Semantically specified target words evoke strong co-rippling between wordform, semantic, executive and response areas from 400 to 800 ms, with increased co-rippling between semantic, executive and response areas prior to correct responses. Co-ripples were phase-locked at zero lag over long distances (>12 cm), especially when many areas were co-rippling. General co-activation, indexed by non-oscillatory high gamma, was mainly confined to early latencies in fusiform and earlier visual areas, preceding co-ripples. These findings suggest that widespread synchronous co-ripples may assist the integration of multiple cortical areas for sustained periods during cognition., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Hoarding disorder (HD) and obsessive-compulsive disorder (OCD) are highly comorbid and genetically related, but their similarities and differences at the neural level are not well characterized. The present study examined the time-frequency information contained in stimulus-related EEG data as participants worked on a visual flanker task. Three groups were included: participants diagnosed with HD (N = 33), OCD (N = 26), and healthy controls (N = 35). Permutation-controlled mass-univariate analyses found no differences between groups in terms of the magnitude of the oscillatory responses. Differences between groups were found selectively for phase-based measures (phase-locking across trials and across sensors) in time ranges well after those consistent with initial visuocortical processes, in the alpha (10 Hz) as well as theta and beta frequency bands, centered around 6 Hz and 15 Hz, respectively. Specifically, HD showed attenuated phase locking in theta and alpha compared to OCD and HC, while OCD showed heightened inter-site phase locking in alpha/beta. Including age as a covariate attenuated, but did not eliminate, the group differences. These findings point to signatures of cortical dynamics and cortical communication task processing that are unique to HD, and which are specifically present during higher-order visual cognition such as stimulus-response mapping, response selection, and action monitoring., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This work was funded by the University of Florida Center for OCD, Anxiety and Related Disorders and the University of Florida Evelyn F. and William L. McKnight Brain Institute. AK and JBF received funding from NIH grant R01 MH125615 to AK. Original data collection was funded through NIH grant R21 MH087748., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Electroencephalograms can capture brain oscillatory activities during sleep as a form of electrophysiological signals. We analysed electroencephalogram recordings from full-night in-laboratory polysomnography from 100 patients with Down syndrome, and 100 age- and sex-matched controls. The ages of patients with Down syndrome spanned 1 month to 31 years (median 4.4 years); 84 were younger than 12 years, and 54 were male. From each electroencephalogram, we extracted relative power in six frequency bands or rhythms (delta, theta, alpha, slow sigma, fast sigma, and beta) from six channels (frontal F3 and F4, central C3 and C4, and occipital O1 and O2) during five sleep stages (N3, N2, N1, R and W)-180 features in all. We examined differences in relative power between Down syndrome and control electroencephalograms for each feature separately. During wake and N1 sleep stages, alpha rhythms (8.0-10.5 Hz) had significantly lower power in patients with Down syndrome than controls. Moreover, the rate of increase in alpha power with age during rapid eye movement sleep was significantly slower in Down syndrome than control subjects. During wake and N1 sleep, delta rhythms (0.25-4.5 Hz) had higher power in patients with Down syndrome than controls. During N2 sleep, slow sigma rhythms (10.5-12.5 Hz) had lower power in patients with DS than controls. These findings extend previous research from routine electroencephalogram studies demonstrating that patients with Down syndrome had reduced circadian amplitude-the difference between wake alpha power and deep sleep delta power was smaller in Down syndrome than control subjects. We envision that these brain oscillatory activities may be used as surrogate markers for clinical trials for patients with Down syndrome., (© 2024 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Objective: In addition to the oscillatory brain activity, the nonoscillatory (scale-free) components of the background electroencephalogram (EEG) may provide further information about the complexity of the underlying neuronal network. As epilepsy is considered a network disease, such scale-free metrics might help to delineate the epileptic network. Here, we performed an analysis of the sleep oscillatory (spindle, slow wave, and rhythmic spectral power) and nonoscillatory (H exponent) intracranial EEG using multiple interictal features to estimate whether and how they deviate from normalcy in 38 adults with drug-resistant epilepsy., Methods: To quantify intracranial EEG abnormalities within and outside the seizure onset areas, patients' values were adjusted based on normative maps derived from the open-access Montreal Neurological Institute open iEEG Atlas. In a subset of 29 patients who underwent resective surgery, we estimated the predictive value of these features to identify the epileptogenic zone in those with a good postsurgical outcome., Results: We found that distinct sleep oscillatory and nonoscillatory metrics behave differently across the epileptic network, with the strongest differences observed for (1) a reduction in spindle activity (spindle rates and rhythmic sigma power in the 10-16 Hz band), (2) a higher rhythmic gamma power (30-80 Hz), and (3) a higher H exponent (steeper 1/f slope). As expected, epileptic spikes were also highest in the seizure onset areas. Furthermore, in surgical patients, the H exponent achieved the highest performance (balanced accuracy of .76) for classifying resected versus nonresected channels in good outcome patients., Significance: This work suggests that nonoscillatory components of the intracranial EEG signal could serve as promising interictal sleep candidates of epileptogenicity in patients with drug-resistant epilepsy. Our findings further advance the understanding of epilepsy as a disease, whereby absence or loss of sleep physiology may provide information complementary to pathological epileptic processes., (© 2024 The Author(s). Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Defective cognition development during preschool years is believed to be linked with core symptoms of autism spectrum disorder (ASD). Neurophysiological research on mechanisms underly the cognitive disabilities of preschool-aged children with ASD is scarce currently. This pilot study aimed to compare the resting spectral EEG power of preschool-aged boys with ASD with their matched typically developing peers. Children in the ASD group demonstrated reduced central and posterior absolute delta (1-4 Hz) and enhanced frontal absolute beta (12-30 Hz) and gamma (30-45 Hz). The relative power of the ASD group was elevated in delta, theta (4-8 Hz), alpha (8-12 Hz), beta, and gamma bands as compared to the controls. The theta/beta ratio decreased in the frontal regions and enhanced at Cz and Pz electrodes in the ASD group. Correlations between the inhibition and metacognition indices of the behavior rating inventory of executive function-preschool version (BRIEF-P) and the theta/beta ratio for children of both groups were significant. In conclusion, the present study revealed atypical resting spectral characteristics of boys with ASD at preschool ages. Future large-sampled studies for the generalization of our findings and a better understanding of the relationships between brain oscillations and phenotypes of ASD are warranted.

Objective: Schizophrenia (SCZ) is a globally prevalent, severe chronic mental disorder, with cognitive dysfunction being one of its core symptoms. Notably, overweight is exceedingly common among individuals with SCZ, and overweight can also impact cognitive function. Therefore, the relationship between overweight and cognition in SCZ is a clinical issue that is in need of research attention., Methods: This study enrolled 77 patients with SCZ, including 36 overweight and 41 non-overweight patients. The Positive and Negative Syndrome Scale (PANSS) was used to assess symptom severity, while cognitive functions were evaluated using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Electroencephalography (EEG) testing was performed, with power spectral analysis conducted across various frequency bands (δ, θ, α, β, low γ, and high γ)., Results: Compared to non-overweight SCZ patients, those overweight exhibited significantly lower RBANS total and index scores in immediate memory, visuospatial/constructional abilities, and delayed memory. EEG spectral analysis revealed that overweight SCZ patients demonstrated significantly lower oscillation power ratios in the β, low γ, and high γ frequency bands compared to their non-overweight counterparts. Correlation analyses indicated a significant positive relationship between β wave activity and RBANS total scores among overweight SCZ patients, suggesting that reduced β power correlates with more severe cognitive dysfunction., Conclusion: Our findings indicate that overweight SCZ patients experience more severe cognitive impairments in a resting state than those who are not overweight, with significant differences in EEG spectrum observed in the β and γ frequency bands. Additionally, our study establishes a correlation between various EEG spectrum dimensions and cognition. This research highlights the effects of overweight on cognition in individuals with SCZ. Additionally, employing EEG technology to study cognitive function in overweight SCZ patients can offer valuable electrophysiological insights., Competing Interests: Declaration of competing interest The authors declare that this study was conducted in the absence of any economic and financial relationships and competing relationships that could be interpreted as potential conflicts of interest, etc., that might influence the work reported herein., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Down syndrome (DS) is the most common cause of intellectual disability, yet little is known about the neurobiological pathways leading to cognitive impairments. Electroencephalographic (EEG) measures are commonly used to study neurodevelopmental disorders, but few studies have focused on young children with DS. Here we assess resting state EEG data collected from toddlers/preschoolers with DS (n = 29, age 13-48 months old) and compare their aperiodic and periodic EEG features with both age-matched (n = 29) and developmental-matched (n = 58) comparison groups. DS participants exhibited significantly reduced aperiodic slope, increased periodic theta power, and decreased alpha peak amplitude. A majority of DS participants displayed a prominent peak in the theta range, whereas a theta peak was not present in age-matched participants. Overall, similar findings were also observed when comparing DS and developmental-matched groups, suggesting that EEG differences are not explained by delayed cognitive ability., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Nicole Baumer reports a relationship with National Down Syndrome Congress that includes: board membership. Nicole Baumer reports a relationship with Down Syndrome Medical Interest Group - USA that includes: board membership. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

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

Background: Sleep deprivation (SD) can impair an individual's alertness, which is the basis of attention and the mechanism behind continuous information processing. However, research concerning the effects of total sleep deprivation (TSD) on alertness networks is inadequate. In this study, we investigate the cognitive neural mechanism of alertness processing after TSD., Methods: Twenty-four college students volunteered to participate in the study. The resting-state electroencephalogram (EEG) data were collected under two conditions (rested wakefulness [RW], and TSD). We employed isolated effective coherence (iCoh) analysis and functional independent component analysis (fICA) to explore the effects of TSD on participants' alertness network., Results: This study found the existence of two types of effective connectivity after TSD, as demonstrated by iCoh: from the left cuneus to the right middle frontal gyrus in the β3 and γ bands, and from the left angular gyrus to the left insula in the δ, θ, α, β1, β3, and γ bands. Furthermore, Pearson correlation analysis showed that increased effective connectivity between all the bands had a positive correlation with increases in the response time in the psychomotor vigilance task (PVT). Finally, fICA revealed that the neural oscillations of the cuneus in the α2 bands increased, and of the angular gyrus in the α and β1 bands decreased in TSD., Conclusions: TSD impairs the alertness function among individuals. Increased effective connectivity from the cuneus to the middle frontal gyrus may represent overloads on the alertness network, resulting in participants strengthening top-down control of the attention system. Moreover, enhanced effective connectivity from the angular gyrus to the insula may indicate a special perception strategy in which individuals focus on salient and crucial environmental information while ignoring inessential stimuli to reduce the heavy burden on the alertness network., Clinical Trial Registration: No: ChiCTR2400088448. Registered 19 August 2024, https://www.chictr.org.cn., (© 2024 The Author(s). Published by IMR Press.)

Spike-and-wave discharges (SWDs) and sleep spindles are characteristic electroencephalographic (EEG) hallmarks of absence seizures and nonrapid eye movement sleep, respectively. They are commonly generated by the cortico-thalamo-cortical network including the thalamic reticular nucleus (TRN). It has been reported that SWD development is accompanied by a decrease in sleep spindle density in absence seizure patients and animal models. However, whether the decrease in sleep spindle density precedes, coincides with, or follows, the SWD development remains unknown. To clarify this, we exploited Pvalb -tetracycline transactivator (tTA)::tetO-ArchT (PV-ArchT) double-transgenic mouse, which can induce an absence seizure phenotype in a time-controllable manner by expressing ArchT in PV neurons of the TRN. In these mice, EEG recordings demonstrated that a decrease in sleep spindle density occurred 1 week before the onset of typical SWDs, with the expression of ArchT. To confirm such temporal relationship observed in these genetic model mice, we used a gamma-butyrolactone (GBL) pharmacological model of SWDs. Prior to GBL administration, we administered caffeine to wild-type mice for 3 consecutive days to induce a decrease in sleep spindle density. We then administered low-dose GBL, which cannot induce SWDs in normally conditioned mice but led to the occurrence of SWDs in caffeine-conditioned mice. These findings indicate a temporal relationship in which the decrease in sleep spindle density consistently precedes SWD development. Furthermore, the decrease in sleep spindle activity may have a role in facilitating the development of SWDs. Our findings suggest that sleep spindle reductions could serve as early indicators of seizure susceptibility., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 Abdelaal et al.)

The relation between electroencephalography (EEG) rhythms, brain functions, and behavioral correlates is well-established. Some physiological mechanisms underlying rhythm generation are understood, enabling the replication of brain rhythms in silico. This offers a pathway to explore connections between neural oscillations and specific neuronal circuits, potentially yielding fundamental insights into the functional properties of brain waves. Information theory frameworks, such as Integrated Information Decomposition (Φ-ID), relate dynamical regimes with informational properties, providing deeper insights into neuronal dynamic functions. Here, we investigate wave emergence in an excitatory/inhibitory (E/I) balanced network of integrate and fire neurons with short-term synaptic plasticity. This model produces a diverse range of EEG-like rhythms, from low δ waves to high-frequency oscillations. Through Φ-ID, we analyze the network's information dynamics and its relation with different emergent rhythms, elucidating the system's suitability for functions such as robust information transfer, storage, and parallel operation. Furthermore, our study helps to identify regimes that may resemble pathological states due to poor informational properties and high randomness. We found, e.g., that in silico β and δ waves are associated with maximum information transfer in inhibitory and excitatory neuron populations, respectively, and that the coexistence of excitatory θ, α, and β waves is associated to information storage. Additionally, we observed that high-frequency oscillations can exhibit either high or poor informational properties, potentially shedding light on ongoing discussions regarding physiological versus pathological high-frequency oscillations. In summary, our study demonstrates that dynamical regimes with similar oscillations may exhibit vastly different information dynamics. Characterizing information dynamics within these regimes serves as a potent tool for gaining insights into the functions of complex neuronal networks. Finally, our findings suggest that the use of information dynamics in both model and experimental data analysis, could help discriminate between oscillations associated with cognitive functions and those linked to neuronal disorders., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Menesse, Torres. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Previous studies have reported that the sense of "self" is associated with specific brain regions and neural network activities. In addition, the mirror system, which functions when executing or observing an action, might contribute to differentiating the self from others and form the basis of the sense of self as a fundamental physical representation. This study investigated whether differences in mu suppression, an indicator of mirror system activity, reflect cognitions related to self-other discrimination., Methods: The participants were 30 of healthy college students. The participants observed short video clips of hand movements performed by themselves or actors from two perspectives (i.e., first-person and third-person). The electroencephalogram (EEG) mu rhythm (8-13 Hz) was measured during video observation as an index of mirror neuron system activity. EEG activity related to self-detection was analyzed using participants' hand movements as self-relevant stimuli., Results: The results showed that mu suppression in the 8-13-Hz range exhibited perspective-dependent responses to self/other stimuli. There was a significant self-oriented mu suppression response in the first-person perspective. However, the study found no significant response orientation in the third-person perspective. The results suggest that mirror system activity may involve self-other discrimination differently depending on the perspective., Conclusions: In summary, this study examined the mirror system's activity for self and others using the EEG's mu suppression. As a result, it was suggested that differences in self and others or perspectives may influence mu suppression., (© 2024. The Author(s).)

Generalized epilepsy (GE) encompasses a heterogeneous group of hyperexcitability disorders that clinically manifest as seizures. At the whole-brain level, distinct seizure patterns as well as interictal epileptic discharges (IEDs) reflect key signatures of hyperexcitability in magneto- and electroencephalographic (M/EEG) recordings. Moreover, it had been suggested that aperiodic activity, specifically the slope of the 1/ ƒ x decay function of the power spectrum, might index neural excitability. However, it remained unclear if hyperexcitability as encountered at the cellular level directly translates to putative large-scale excitability signatures, amenable to M/EEG. In order to test whether the power spectrum is altered in hyperexcitable states, we recorded resting-state MEG from male and female GE patients ( n  = 51; 29 females; 28.82 ± 12.18 years; mean ± SD) and age-matched healthy controls ( n  = 49; 22 females; 32.10 ± 12.09 years). We parametrized the power spectra using FOOOF ("fitting oscillations and one over f ") to separate oscillatory from aperiodic activity to directly test whether aperiodic activity is systematically altered in GE patients. We further identified IEDs to quantify the temporal dynamics of aperiodic activity around overt epileptic activity. The results demonstrate that aperiodic activity indexes hyperexcitability in GE at the whole-brain level, especially during epochs when no IEDs were present ( p  = 0.0130; d  = 0.52). Upon IEDs, large-scale circuits transiently shifted to a less excitable network state ( p  = 0.001; d  = 0.68). In sum, these results uncover that MEG background activity might index hyperexcitability based on the current brain state and does not rely on the presence of epileptic waveforms., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 Kopf et al.)

Recent studies demonstrate that behavioral performance during visual spatial attention fluctuates at theta (4 to 8 Hz) and alpha (8 to 16 Hz) 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 1,300 ms) 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 (4 Hz) under certain predictions and at alpha & beta bands (15 & 19 Hz) 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., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Despite advances in understanding the cellular and molecular processes underlying memory and cognition, and recent successful modulation of cognitive performance in brain disorders, the neurophysiological mechanisms remain underexplored. High frequency oscillations beyond the classic electroencephalogram spectrum have emerged as a potential neural correlate of fundamental cognitive processes. High frequency oscillations are detected in the human mesial temporal lobe and neocortical intracranial recordings spanning gamma/epsilon (60-150 Hz), ripple (80-250 Hz) and higher frequency ranges. Separate from other non-oscillatory activities, these brief electrophysiological oscillations of distinct duration, frequency and amplitude are thought to be generated by coordinated spiking of neuronal ensembles within volumes as small as a single cortical column. Although the exact origins, mechanisms and physiological roles in health and disease remain elusive, they have been associated with human memory consolidation and cognitive processing. Recent studies suggest their involvement in encoding and recall of episodic memory with a possible role in the formation and reactivation of memory traces. High frequency oscillations are detected during encoding, throughout maintenance, and right before recall of remembered items, meeting a basic definition for an engram activity. The temporal coordination of high frequency oscillations reactivated across cortical and subcortical neural networks is ideally suited for integrating multimodal memory representations, which can be replayed and consolidated during states of wakefulness and sleep. High frequency oscillations have been shown to reflect coordinated bursts of neuronal assembly firing and offer a promising substrate for tracking and modulation of the hypothetical electrophysiological engram., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

We investigated memory encoding and retrieval during a quasinaturalistic spatial-episodic memory task in which subjects delivered items to landmarks in a desktop virtual environment and later recalled the delivered items. Transition probabilities and latencies revealed the spatial and temporal organization of memory. As subjects gained experience with the town, their improved spatial knowledge led to more efficient navigation and increased spatial organization during recall. Subjects who exhibited stronger spatial organization exhibited weaker temporal organization. Scalp-recorded electroencephalographic signals revealed spectral correlates of successful encoding and retrieval. Increased theta power (T+) and decreased alpha/beta power (A-) accompanied successful encoding, with the addition of increased gamma (G+) accompanying successful retrieval. Logistic regression classifiers trained on spectral features reliably predicted mnemonic success in held-out sessions. Univariate and multivariate electroencephalographic analyses revealed a similar spectral T+A-G+ of successful memory. These findings extend behavioral and neural signatures of successful encoding and retrieval to a naturalistic task in which learning occurs within a spatiotemporal context. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Cognitive impairments are common in Parkinson's disease (PD). We have linked this deficit to attenuated midfrontal 1-8-Hz activity that fails to engage cortical cognitive networks. We discuss the consequences of these impairments and how they might be leveraged for PD-specific neurophysiological markers and for novel brain stimulation paradigms., Competing Interests: Declaration of interests No interests are declared., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Parkinson's Disease (PD) body-first subtype is characterized by prodromal autonomic symptoms and REM sleep behavior disorder (RBD), symmetric dopaminergic degeneration, and increased risk of dementia. On the other hand, the PD brain-first subtype has fewer non-motor symptoms and a milder motor phenotype. The temporal relationship between RBD onset and motor symptoms onset may differentiate these two subtypes. We aimed to investigate electrocortical differences between brain-first and body-first PD patients. PD patients with an available routinely collected EEG were retrospectively selected. RBD was diagnosed using the RBD screening questionnaire (≥ 6). According to the onset of RBD patients were classified into PD-RBDpre (RBD onset before motor symptoms) and PD-RBDpost (RBD onset after motor symptoms). Patients without RBD were classified as PD-RBD-. Presence of Mild Cognitive Impairment (MCI) was diagnosed according to the MDS criteria. EEG Spectral analysis was performed in resting state by computing the Power Spectral Density (PSD) of site-specific signal epochs for the common frequency bands (delta, theta, alpha, beta). Thirty-eight PD-RBD-, 14 PD-RBDpre and 31 PD-RBDpost patients were recruited. Comparing both global and site-specific absolute values, we found a significant trend toward beta band reduction going from PD-RBD-, PD-RBDpost and PD-RBDpre. No significant differences were found between PD-RBDpost and PD-RBD- patients. PD-RBDpre patients may represent a different subset of patients as compared to patients without RBD, while patients with later onset have intermediate EEG spectral features. Quantitative EEG may provide new hints in PD subtyping., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.)

It is known that Temporal Information Processing (TIP) underpins our cognitive functioning. Previous research has focused on the relationship between TIP efficiency and oscillatory brain activity, especially the gamma rhythm; however, non-oscillatory (aperiodic or 1/f) brain activity has often been missed. Recent studies have identified the 1/f component as being important for the functioning of the brain. Therefore, the current study aimed to verify whether TIP efficiency is associated with specific EEG resting state cortical activity patterns, including oscillatory and non-oscillatory (aperiodic) brain activities. To measure individual TIP efficiency, we used two behavioral tasks in which the participant judges the order of two sounds separated by millisecond intervals. Based on the above procedure, participants were classified into two groups with high and low TIP efficiency. Using cluster-based permutation analyses, we examined between-group differences in oscillatory and non-oscillatory (aperiodic) components across the 1-90 Hz range. The results revealed that the groups differed in the aperiodic component across the 30-80 Hz range in fronto-central topography. In other words, participants with low TIP efficiency exhibited higher levels of aperiodic activity, and thus a flatter frequency spectrum compared to those with high TIP efficiency. We conclude that participants with low TIP efficiency display higher levels of 'neural noise', which is associated with poorer quality and speed of neural processing., Competing Interests: Declaration of Competing Interest The authors declare that the research was conducted in the absence of any commercial or financial and nonfinancial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: To use intracranial electroencephalography (EEG) to characterize functional magnetic resonance imaging (fMRI) activation maps associated with high-frequency oscillations (HFOs) (80-250 Hz) and examine their proximity to HFO- and seizure-generating tissue., Methods: Forty-five patients implanted with intracranial depth electrodes underwent a simultaneous EEG-fMRI study at 3 T. HFOs were detected algorithmically from cleaned EEG and visually confirmed by an experienced electroencephalographer. HFOs that co-occurred with interictal epileptiform discharges (IEDs) were subsequently identified. fMRI activation maps associated with HFOs were generated that occurred either independently of IEDs or within ±200 ms of an IED. For all significant analyses, the Maximum, Second Maximum, and Closest activation clusters were identified, and distances were measured to both the electrodes where the HFOs were observed and the electrodes involved in seizure onset., Results: We identified 108 distinct groups of HFOs from 45 patients. We found that HFOs with IEDs produced fMRI clusters that were closer to the local field potentials of the corresponding HFOs observed within the EEG than HFOs without IEDs. In addition to the fMRI clusters being closer to the location of the EEG correlate, HFOs with IEDs generated Maximum clusters with greater z-scores and larger volumes than HFOs without IEDs. We also observed that HFOs with IEDs resulted in more discrete activation maps., Significance: Intracranial EEG-fMRI can be used to probe the hemodynamic response to HFOs. The hemodynamic response associated with HFOs that co-occur with IEDs better identifies known epileptic tissue than HFOs that occur independently., (© 2024 The Author(s). Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Background: The limited understanding of the physiology and psychology of polar expedition explorers has prompted concern over the potential cognitive impairments caused by exposure to extreme environmental conditions. Prior research has demonstrated that such stressors can negatively impact cognitive function, sleep quality, and behavioral outcomes. Nevertheless, the impact of the polar environment on neuronal activity remains largely unknown., Methods: In this study, we aimed to investigate spatiotemporal alterations in brain oscillations of 13 individuals (age range: 22-48 years) who participated in an Arctic expedition. We utilized electroencephalography (EEG) to record cortical activity before and during the Arctic journey, and employed standardized low resolution brain electromagnetic tomography to localize changes in alpha, beta, theta, and gamma activity., Results: Our results reveal a significant increase in the power of theta oscillations in specific regions of the Arctic, which differed significantly from pre-expedition measurements. Furthermore, microstate analysis demonstrated a significant reduction in the duration of microstates (MS) D and alterations in the local synchrony of the frontoparietal network., Conclusion: Overall, these findings provide novel insights into the neural mechanisms underlying adaptation to extreme environments. These findings have implications for understanding the cognitive consequences of polar exploration and may inform strategies to mitigate potential neurological risks associated with such endeavors. Further research is warranted to elucidate the long-term effects of Arctic exposure on brain function., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

We investigated the differences in functional connectivity based on the source-level electroencephalography (EEG) analysis between stroke patients with and without post-stroke epilepsy (PSE). Thirty stroke patients with PSE and 35 stroke patients without PSE were enrolled. EEG was conducted during a resting state period. We used a Brainstorm program for source estimation and the connectivity matrix. Data were processed according to EEG frequency bands. We used a BRAPH program to apply a graph theoretical analysis. In the beta band, radius and diameter were increased in patients with PSE than in those without PSE (2.699 vs. 2.579, adjusted p = 0.03; 2.261 vs. 2.171, adjusted p = 0.03). In the low gamma band, radius was increased in patients with PSE than in those without PSE (2.808 vs. 2.617, adjusted p = 0.03). In the high gamma band, the radius, diameter, average eccentricity, and characteristic path length were increased (1.828 vs. 1.559, adjusted p < 0.01; 2.653 vs. 2.306, adjusted p = 0.01; 2.212 vs. 1.913, adjusted p < 0.01; 1.425 vs. 1.286, adjusted p = 0.01), whereas average strength, mean clustering coefficient, and transitivity were decreased in patients with PSE than in those without PSE (49.955 vs. 55.055, adjusted p < 0.01; 0.727 vs. 0.810, adjusted p < 0.01; 1.091 vs. 1.215, adjusted p < 0.01). However, in the delta, theta, and alpha bands, none of the functional connectivity measures were different between groups. We demonstrated significant alterations of functional connectivity in patients with PSE, who have decreased segregation and integration in brain network, compared to those without PSE., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy., Methods: MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results., Results: Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p<0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ., Conclusion: HFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Neurofeedback training (NFT) is a promising adjuvant intervention method. The desynchronization of mu rhythm (8-13 Hz) in the electroencephalogram (EEG) over centro-parietal areas is known as a valid indicator of mirror neuron system (MNS) activation, which has been associated with social skills. Still, the effect of neurofeedback training on the MNS requires to be well investigated. The present study examined the possible impact of NFT with a mu suppression training protocol encompassing 15 NFT sessions (45 min each) on 16 healthy neurotypical participants. In separate pre- and post-training sessions, 64-channel EEG was recorded while participants (1) observed videos with various types of movements (including complex goal-directed hand movements and social interaction scenes) and (2) performed the "Reading the Mind in the Eyes Test" (RMET). EEG source reconstruction analysis revealed statistically significant mu suppression during hand movement observation across MNS-attributed fronto-parietal areas after NFT. The frequency analysis showed no significant mu suppression after NFT, despite the fact that numerical mu suppression appeared to be visible in a majority of participants during goal-directed hand movement observation. At the behavioral level, RMET accuracy scores did not suggest an effect of NFT on the ability to interpret subtle emotional expressions, although RMET response times were reduced after NFT. In conclusion, the present study exhibited preliminary and partial evidence that mu suppression NFT can induce mu suppression in MNS-attributed areas. More powerful experimental designs and longer training may be necessary to induce substantial and consistent mu suppression, particularly while observing social scenarios., (© 2024. The Author(s).)

As the older population continues to expand, there is a growing prevalence of individuals who experience subjective cognitive decline (SCD), characterized by self-reported failures in cognitive function and an increased risk of cognitive impairment. Recognizing that preventive interventions are typically more effective in preclinical stages, current research endeavors to focus on identifying early biological markers of SCD using resting-state electroencephalogram (rsEEG) methods. To do so, a systematic literature review covering the past 20 years was conducted following PRISMA guidelines, in order to consolidate findings on rsEEG frequency bands in individuals with SCD. Pubmed and Web of Science databases were searched for rsEEG studies of people with SCD. Quality assessments were completed using a modified Newcastle-Ottawa scale. A total of 564 articles published from December 2003 to December 2023 were reviewed, and significant aspects of these papers were analyzed to provide a general overview of the research on this technique. After removing unrelated articles, nine articles were selected for the present study. The review emphasizes patterns in frequency band activity, revealing that individuals classified as SCD exhibited increased theta power than healthy controls, but decreased than MCI. However, findings for the alpha, delta, and beta bands were inconsistent, demonstrating variability across studies and highlighting the need for further research. Although the rsEEG of frequency bands emerges as a promising early biomarker, there is a noteworthy need to establish uniform standards and consistent measurement approaches in order to ensure the reliability and comparability of the results obtained in the research., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Neurological disorders arise primarily from the dysfunction of brain cells, leading to various impairments. Electroencephalography (EEG) stands out as the most popular method in the discovery of neuromarkers indicating neurological disorders. The proposed study investigates the effectiveness of spectral and synchrony neuromarkers derived from resting state EEG in the detection of Mild Cognitive Impairment (MCI) with controls., New Methods: The dataset is composed of 10 MCI and 10 HC groups. Spectral features and synchrony measures are utilized to detect slowing patterns in MCI. Efficient neuro-markers are classified by 25 classification algorithm. Independent samples t-test and Pearson's Correlation Coefficients are applied to reveal group differences for spectral markers, and repeated measures ANOVA is tested for wPLI-based markers., Results: Lower peak amplitudes are prominent in MCI participants for high frequencies indicating slower physiological behavior of the demented EEG. The MCI and HC groups are correctly classified with 95 % acc. using peak amplitudes of beta band with LGBM classifier. Higher wPLI values are calculated for HC participants in high frequencies. The alpha wPLI values achieve a classification accuracy of 99 % using the LGBM algorithm for MCI detection., Comparison With Existing Methods: The neuro-markers including peak amplitudes, frequencies, and wPLIs with advanced machine learning techniques showcases the innovative nature of this research., Conclusion: The findings suggest that peak amplitudes and wPLI in high frequency bands derived from resting state EEG are effective neuromarkers for detection of MCI. Spectral and synchrony neuro-markers hold great promise for accurate MCI detection., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Ketamine is an NMDA receptor antagonist that has antidepressant and anesthetic properties. At subanesthetic doses, ketamine induces transient psychosis in humans, and is used to model psychosis in experimental animals. In rodents, subanesthetic doses of ketamine increase the power of high-frequency oscillations (HFO, > 100 Hz) in the electroencephalogram (EEG), a frequency band linked to cognitive functions. However, to date, the effects of ketamine in carnivores and primates have been poorly investigated. Here, we examined in the cat, cortical HFO during wakefulness, sleep, and after administering a sub-anesthetic dose of ketamine. Four cats were prepared with cortical electrodes for chronic polysomnographic recordings in head-restrained conditions. The cortical HFO power, connectivity, direction of the information flow using Granger Causality (GC) analysis, their relationships with respiratory activity, and the effect of auditory stimulation were analyzed. During wakefulness, but not during sleep, we found that HFO were coupled with the inspiratory phase of the respiration. After ketamine administration, HFO power was enhanced and remained associated with the inspiratory phase. GC analysis suggests that ketamine-enhanced HFO originate from the olfactory bulb (OB) and stream towards the prefrontal cortex (Pf). Accordingly, occluding the nostrils significantly reduced the power of the ketamine-enhanced HFO in both the OB and Pf. Finally, auditory stimulation did not affect HFO. In conclusion, the HFO are associated with respiration during wakefulness, but not during sleep. The enhancement of this rhythm by ketamine may disrupt cortical information processing, which could contribute to some of the neuropsychiatric effects associated with ketamine., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Working memory is vital for short-term information processing. Binaural beats can enhance working memory by improving attention and memory consolidation through neural synchronization. However, individual differences in cognitive and neuronal functioning affect effectiveness of binaural beats, necessitating personalized approaches. This study aimed to develop a machine learning model to predict binaural beats's effectiveness on working memory using electroencephalography. Sixty healthy participants underwent a 5-min electroencephalography recording, an initial working memory evaluation, 15 min of binaural beats stimulation, and a subsequent working memory evaluation using digit span tests of increasing difficulty. Recall accuracy and response times were measured. Differential scores from pre-evaluation and post-evaluation labeled participants as active or inactive to binaural beats stimulation. electroencephalography data, recorded using 14 electrodes, provided brain activity estimates across theta, alpha, beta, and gamma frequency bands, resulting in 56 features (14 channels × 4 bands) for the machine learning model. Several classifiers were tested to identify the most effective model. The weighted K-nearest neighbors model achieved the highest accuracy (90.0%) and area under the receiver operating characteristic curve (92.24%). Frontal and parietal electroencephalography channels in theta and alpha bands were crucial for classification. This study's findings offer significant clinical insights, enabling informed interventions and preventing resource inefficiency., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Auditory beats stimulation (ABS) has received increased attention for its potential to modulate neural oscillations through a phenomenon described as brain entrainment (i.e synchronization of brain's electrocortical activity to external stimuli at a specific frequency). Recently, a new form of ABS has emerged, inspired by isochronic tones stimulation (IT d ). This study investigated neural oscillatory responses induced by IT d in comparison with formerly well-established ABS protocols, such as gamma-binaural beats (BB) and white noise (WN). We recorded the electroencephalographic brain activity in 28 participants during 4 min of BB, IT d , and WN presentation. Data demonstrated that while both BB and WN enhanced oscillatory power on the EEG gamma band, consistently with the expected brain entrainment effect, IT d yielded greater changes in EEG power (p < 0.001). This was confirmed by time-based analysis, which showed a progressive increase in normalized EEG power within the IT d window compared to BB (p < 0.05). Findings also revealed that IT d elicited acute changes in the alpha band of EEG oscillations, through a progressive decrease in power over time, which was distinctly different from the pattern observed while listening BB and WN. Such dual alpha-gamma effects underline the promising and unique potential of IT d to modulate neural oscillations which selectively differ from BB and WN. This study contributes to the evolution of ABS research, highlighting the promise of IT d for cognitive enhancement and clinical applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 IBRO. Published by Elsevier Inc. All rights reserved.)