Specific Frequency Bands Research Articles

Objective. Several data-driven approaches based on information theory have been proposed for analyzing high-order interactions (HOIs) involving three or more components of a network system. The existing methods do not account for temporal correlations in the data, or are defined only in the time domain and rely on the assumption of stationarity in the underlying dynamics, making them inherently unable to detect frequency-specific behaviors and track transient functional links in physiological networks.Approach. This study introduces a new framework which enables the time-varying and time-frequency analysis of HOIs in networks of random processes through the spectral representation of vector autoregressive models. The time- and frequency-resolved analysis of synergistic and redundant interactions among groups of processes is ensured by a robust identification procedure based on a recursive least squares estimator with a forgetting factor.Main results. Validation on simulated networks illustrates how the time-frequency analysis is able to highlight transient synergistic behaviors emerging in specific frequency bands which cannot be detected by time-domain stationary analyzes. The application on brain evoked potentials in rats elicits the presence of redundant information timed with whisker stimulation and mostly occurring in the contralateral hemisphere. The application to cardiovascular oscillations reveals a reduction in redundant information following head-up tilt, reflecting a functional disconnection within the physiological network of heart period, respiratory, and arterial pressure signals.Significance. The proposed framework enables a comprehensive time-varying and time-frequency analysis of the hierarchical organization of dynamic networks. As our approach goes beyond pairwise interactions, it is well suited for the study of transient high-order behaviors arising during state transitions in many network systems commonly studied in physiology, neuroscience and other fields.

Introduction The functional role of the ipsilateral primary motor cortex (iM1) activation in motor skill acquisition is widely researched; however, its interaction with task complexity remains unclear. This study aimed to address a critical gap in motor neuroscience: how the electroencephalogram (EEG) activation dynamics (specifically in the gamma frequency band) recorded by electrodes over the contralateral primary motor cortex (cM1) and iM1 evolve during the acquisition of simple vs. complex motor skills, and whether these dynamics are modulated by hand dominance. Methods In a randomized controlled trial, 48 right-handed participants were randomly assigned to train on either simple or complex visuomotor tasks using their right (SR, CR, respectively) or left hand (SL, CL, respectively), with 12 participants per group. One participant in the SL group was excluded due to poor EEG quality, resulting in 11 participants in the SL group. Participants completed 10 training blocks followed by skill retention tests. Brain activity was recorded continuously via 64-channel EEG. Results Data from 47 participants revealed that, prior to training, the high-gamma band (50-80Hz) activation recorded by electrodes over iM1 exhibited significantly higher activation during simple tasks compared to complex tasks, irrespective of the hand used. However, after 10 training sessions, the electrodes over iM1 activation increased during complex tasks but decreased during simple task for both hands, eliminating significant differences in activation levels between simple and complex tasks. Furthermore, no significant changes were observed in the EEG activation recorded by electrodes over cM1 before and after training. Conclusions Our data indicated that task complexity affects the EEG activation identified from electrodes over iM1. Specifically, complex task training for both right and left hands enhanced the high-gamma frequency band power recorded from the electrodes over iM1. These findings highlight differential neural responses within specific frequency bands, potentially reflecting distinct impacts of the interventions applied to each group. This supports the idea that iM1 plays a dynamic, task-dependent role in skill acquisition, consistent with prior proposals that iM1 activation scales with task demands.

This study investigated the correlation between subjective preferences for different cosmetic formulations and brain activity measured using electroencephalography (EEG). EEG data were collected from 29 participants when they applied three positive and one negative cosmetic formulation to the inside of their left forearms. According to the questionnaire results, the negative formulation showed significantly lower preference scores than the positive formulations. Additionally, significant EEG-preference correlations were consistently found in the delta and alpha bands within the sensorimotor areas closely related to tactile processing and its emotional regulation. In particular, stronger correlations were observed when only the two positive formulations with higher preferences were included in the analysis or when specific frequency bands showing significant results were combined together. These findings demonstrate the potential of predicting cosmetic preferences based on EEG data and highlight the crucial role of texture sensation in shaping user choice.

The primary objective of this study was to examine changes in brain network architecture across multiple frequency bands using spectral analysis of both weighted and binarized functional connectivity networks. This cross-sectional observational study, conducted as a secondary analysis of a publicly available EEG dataset, analyzed spectral coherence measurements from 25 patients with Alzheimer's disease (AD) and 25 age- and sex-matched healthy controls (HC). Nevertheless, the modest sample size and cultural homogeneity of the dataset may limit the statistical power and generalizability of the results. A data-driven thresholding approach was employed to generate binary networks, allowing a robust comparison of connectivity disruptions associated with AD. Brain network features derived from the graph Laplacian, including weighted Fiedler value, spectral range, and Middle Eigenvalue, were analyzed across seven frequency layers: delta, theta, alpha1, alpha2, beta1, beta2, and gamma. For binary networks, the Fiedler value was calculated after thresholding. Statistical group comparisons between AD and HC were performed using t-tests (p < 0.05), and each feature was assessed based on the number of frequency bands showing significant differences. Among all features, the weighted Fiedler value was the most discriminative, showing significant reductions in AD patients within the alpha2 and beta1 bands. In binary networks, the Fiedler value remained significantly lower in AD within the alpha2 band, confirming topological degradation even without edge weight information. Other spectral features showed similar trends, but did not reach statistical significance in the binary networks. The consistent decline in Fiedler value across both weighted and binary networks indicates a global reduction in connectivity characteristic of AD. These spectral markers offer a quantitative and interpretable framework for understanding the progressive disconnection syndrome in AD. This study demonstrates significant alterations in Laplacian spectral features of brain networks between the AD and HC groups across specific frequency bands. These exploratory findings indicate that the spectral features, particularly the Fiedler value, consistently differentiate AD patients from healthy controls across frequency bands, suggesting its potential as a biomarker. However, larger and longitudinal studies are needed to confirm its diagnostic and prognostic utility. The combined use of weighted and binarized connectivity matrices enhances analytical sensitivity and facilitates the application of spectral graph theory for the early detection and monitoring of AD.

BackgroundElectroencephalogram (EEG) microstates provide insights into large-scale brain network coordination, revealing distinct neural dynamics within specific frequency bands associated with cognitive processes and neurological disorders. Critical gaps remain regarding the abnormalities of narrowband microstate networks in Parkinson’s disease with mild cognitive impairment (PD-MCI), a key prodromal stage of the development of PD dementia. Given the importance of early detection and understanding of cognitive decline in PD-MCI, this study investigated whether alterations in narrowband EEG microstate networks could serve as early electrophysiological biomarkers for cognitive decline in PD-MCI.MethodForty-seven individuals with PD (21 with MCI and 26 cognitively normal [PD-NC]) and 20 healthy controls were recruited. For both broadband and narrowband EEG microstates, the phase lag index was used to construct microstate brain networks, and their spatiotemporal variability was assessed.ResultsMicrostate analysis revealed significant divergence in narrowband parameters exclusively between the PD-MCI and PD-NC cohorts. PD-MCI showed a significant increase in low-frequency (delta/alpha-band) microstate class A, while delta-band microstate class D exhibited a significant reduction. The microstate network patterns of PD-MCI were characterized by diminished stability and disrupted synchronization in delta microstate class A within the frontal region, theta microstate class D within central region, and theta microstate class B within the occipital region. These neurophysiological markers specific to PD-MCI were significantly correlated with Montreal Cognitive Assessment scores, and machine learning-based analyses further validated their diagnostic efficacy, with accuracy ranging from 94 to 98%.ConclusionsThis study identified unique abnormalities in narrowband microstate dynamics within neural networks of individuals with PD-MCI, revealing promising electrophysiological markers for the early detection and longitudinal monitoring of cognitive decline. Furthermore, these findings suggest potential applications in precision rehabilitation, whereby frequency-specific microstate biomarkers could guide individualized interventions and monitor therapeutic efficacy.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12984-025-01747-0.

IntroductionThe autonomic nervous system (ANS) is crucial for maintaining homeostasis in the body and plays an important role in cardiovascular diseases. Although heart rate variability (HRV) is a commonly used non-invasive clinical tool to evaluate the function of ANS, it mainly reflects cardiac activity, and it is difficult to comprehensively capture the functional information of peripheral vascular regulation of ANS.MethodsThis study explored the feasibility of using peripheral blood flow signals to evaluate the function of ANS. The ANS function of healthy subjects was artificially intervened by giving glucose solutions at different temperatures. Subsequently, the correlation between peripheral blood flow signals and HRV was further explored. Finally, the quantitative relationship was verified by using an independent dataset.ResultsCoherence analysis shows that within a specific frequency band, the peak values of peripheral blood flow signals are significantly correlated with HRV.DiscussionThis study shows that peripheral blood flow signals analysis provides a new non-invasive way to evaluate the function of ANS. This method not only complements the limitations of traditional HRV analysis, but also hopes to promote the construction of a more comprehensive and multi-dimensional ANS functional evaluation system.

Verbal learning in logopenic variant Primary Progressive Aphasia: An EEG investigation.

Understanding the functional modulation of ion channels by multiple activating substances is critical to grasping stimulus-specific gating mechanisms and possible synergistic or competitive interactions. This study investigates the activation of large-conductance, voltage- and Ca2+-activated potassium channels (BK) in the plasma membrane of human bronchial epithelial cells by Ca2+ and quercetin (Que), both individually and in combination. Patch-clamp recordings were analyzed using open state probability, dwell-time distributions, Shannon entropy, sample entropy, power spectral density (PSD), and empirical mode decomposition (EMD). Our results reveal concentration-dependent alterations in gating kinetics, particularly at a low concentration of quercetin ([Que] = 10 μM) compared with [Que] = 100 μM, where some Que-related effects are strongly attenuated in the presence of Ca2+. We also identify specific frequency bands where oscillatory components are most sensitive to the considered stimuli. Our findings highlight the complex reciprocal interplay between Ca2+ and Que in modulating BK channel function, and demonstrate the interpretative power of entropic and signal-decomposition approaches in characterizing stimulus-specific gating dynamics.

In order to establish a general design methodology for water-based electromagnetic metamaterial absorber microstructures, a topology optimization method for water-based metamaterial absorber microstructures design was proposed in this paper. According to Mie resonance and impedance matching theory, the realization mechanism and physical model of the broadband water-based metamaterial absorber were constructed. The highest average in-band absorption rate was taken as the design object; the topological optimization model for water-based metamaterial absorber design was established. A metamaterial absorber microstructure with 16 discretized water columns inside the unit cell was designed as an example. The obtained structure exhibited a very high average in band absorption rate in the specific frequency band. The proposed method was a collaborative optimization approach that employed a single type of design variable, namely water column height, to simultaneously adjust surface impedance matching and specific resonant modes. It provided a feasible method for achieving the highest average absorption rate within a specific band.

Acoustic metamaterials are artificial structures, often lattice of resonators, with unusual properties. They can be engineered to stop wave propagation in specific frequency bands. Once manufactured, their dispersive qualities remain invariant in time and space, limiting their practical use. Actively tuned arrangements have received growing interest to address this issue. Here, we introduce a new class of active metamaterial made from dual-state unit cells, either vibration sources when powered or passive resonators when left disconnected. They possess self-tuning capabilities, enabling deep subwavelength band gaps to automatically match the carrier signal of powered cells, typically around 200 Hz. Swift electronic commutations between both states establish the basis for real-time reconfiguration of waveguides and shaping of vibration patterns. A series of experiments highlight how these tailored acceleration fields can spatially encode information relevant to human touch. This novel metamaterial can readily be made using off-the-shelf smartphone vibration motors, paving the way for a widespread adoption of multi-touch tactile displays.

Variational autoencoders (VAEs) have been widely used for node clustering, with existing methods mainly focusing on enhancing the expressiveness of their latent space. Recently, the integration of diffusion models with VAEs has provided new opportunities to achieve this objective. However, the mechanism by which the diffusion model improves performance remains unclear. To bridge this gap, we conduct an empirical analysis from the perspective of graph spectral theory, revealing that the signal modulation induced by diffusion models closely aligns with the low-frequency spectral characteristics of VAEs, which in turn explains their effectiveness. Nevertheless, further experiments highlight that diffusion models exhibit limitations in modulating high-frequency signals, which diverge from the spectral characteristics of VAEs. Moreover, existing diffusion methods fail to enable the latent space to adequately capture and reflect cluster-specific characteristics. To address these challenges, we propose a novel plug-and-play method, FVD, to improve the performance of VAE-based methods in node clustering tasks. Specifically, we incorporate the graph wavelet transform as a secondary signal modulator, enabling independent adjustments of specific frequency bands to better align with the spectral characteristics of VAEs. Additionally, we introduce the Student's t-distribution as a conditional constraint in the reverse process of FVD, deriving a more compact variational lower bound. This enhancement preserves fine-grained node information while focusing on clustering details, effectively mitigating the cluster collapse phenomenon. Comprehensive experimental results demonstrate that integrating FVD with existing methods achieves competitive performance improvements in most cases.

Aging disrupts brain network integration and is a significant risk factor forcognitive decline and neurological diseases, yet the circuit-level mechanisms underlyingthese changes remain unclear. Most previous studies have utilized cross-sectional or acuteapproaches, limiting insights into the longitudinal dynamics of the neural network. Inthis study, we chronically recorded laminar electrophysiological activity in both theprimary visual cortex (V1) and hippocampal CA1 region of young (2-month-old) and aged(13-month-old) mice over 16 weeks. This approach allowed us to directly assess how agingmodulates functional connectivity within hierarchically connected cortical and hippocampalcircuits. We found that single-unit spiking activity and the signal-to-noise ratio werelargely preserved in aged versus young mice, suggesting intact neuronal firing properties.However, aged mice showed global reductions in local field potential (LFP) power and aselective decrease in coherence across delta, alpha-beta, and gamma frequency bands withinand between cortical layers and V1-CA1 pathways, while phase amplitude coupling remainedunaffected. Interestingly, population level excitatory activity in CA1 was increased inaged animals. These findings indicate that aging selectively impairs network-levelsynchrony and temporal coordination in specific frequency bands and regions, with minimalloss of single-neuron function. Our results highlight the necessity of longitudinal,multi-region measurements to uncover the multi-scale vulnerabilities of the aging brain.Understanding the depth- and region-dependent circuit changes will guide strategies topreserve cortical-hippocampal communication and cognitive function in aging, as well asenhance neural interface technologies for older populations.

Mu and beta power effects of fast response trait double dissociate during precue and movement execution in the sensorimotor cortex.

This study examines how auditory spectral representations in the peripheral auditory system explain changes in vowel production under noisy conditions, especially when lower formants (F1 and F2) are masked. Ten adult male Japanese speakers produced sustained vowels /a/ and /i/ under quiet and noisy conditions involving three noise types (broadband, low-pass, and high-pass) at 75 and 85 dB. We analyzed vocal intensity and the amplitudes and frequencies of the F1 and F2. Auditory spectral representations, simulated using a loudness model, were used to estimate excitation patterns in the auditory periphery. Most noise conditions significantly increased vocal intensity and the amplitude of both formants. F1 frequency consistently shifted upward under high-intensity broadband noise, while F2 shifts depended on vowel and noise type, shifting upward for /a/ and downward for /i/. Some patterns could not be explained by power spectra alone. Instead, they were better accounted for by frequency-specific masking effects, reflected in overlapping excitation patterns in the auditory spectral representation. These overlaps indicated reduced self-audibility in specific frequency bands, triggering compensatory adjustments. The findings highlight how auditory masking influences speech production, supporting a perceptually grounded model of auditory-motor control in noisy environments.

Behavioral assessments and differential excitability, oscillatory dynamics in dorsal and ventral hippocampal CA1 neurons in male rats of a prenatal VPA-exposed autism model.

Electroencephalogram (EEG) Based Prediction of Attention Deficit Hyperactivity Disorder (ADHD) Using Machine Learning

The amplitude of low-frequency fluctuations (ALFF) and its related measure, fractional ALFF (fALFF), are widely used resting-state fMRI techniques for quantifying spontaneous neural activity within specific frequency bands. However, inconsistencies in the definition and implementation of ALFF have led to confusion in the field. In this study, we provide a mathematical clarification of ALFF and fALFF by introducing two variants: the arithmetic mean-defined ALFF/fALFF (amALFF/amfALFF) and the quadratic mean-defined ALFF/fALFF (qmALFF/qmfALFF). We examine the relationships between mean BOLD intensity (MBI), amALFF, and qmALFF across both subjects and voxels using two independent datasets mapped onto different brain templates. Additionally, we investigate the impact of z-scoring the original BOLD signal on ALFF and fALFF metrics. Our key findings include: (1) MBI is positively correlated with both amALFF and qmALFF, highlighting the need for normalization to subject-level means; (2) normalized qmALFF and qmfALFF are highly correlated with normalized amALFF and amfALFF, respectively, at both the subject and voxel levels; (3) z-scoring the BOLD signal does not affect amfALFF or qmfALFF, but it substantially alters amALFF and qmALFF. Based on these findings, we present a comprehensive flowchart of the (f)ALFF algorithm implemented in the temporal domain. The full procedure is implemented in R, and the corresponding script is available at: https://github.com/lejianhuang/ALFF.

The fluid solid coupling effect in the flow microchannel system can easily induce severe vibration and noise, which seriously affects the performance and safety of the equipment. By its bandgap characteristics, the phononic crystal provides a new way to suppress the propagation of elastic waves in specific frequency bands. In this study, the vibration suppression of fluid-solid coupled phononic crystal microchannels under shock excitation is addressed. Compared with the inadequacy of the existing bandgap calculation methods in fluid computation, this study innovatively combines the transfer matrix method with the wave-finite element method to establish a fluid-solid coupled dynamics model and perform a systematic analysis. The significant effects of fluid filling on the bandgap characteristics are revealed: the unfilled microchannels show two bandgaps (70–90 Hz, 280–690 Hz) in 0–800 Hz; the bandgaps evolve to three (40–65 Hz, 180–340 Hz, 485–735 Hz) after fluid filling. At the same time, the transient vibration propagation and attenuation mechanisms of the system under different fluid shock excitations are deeply investigated. It is shown that the flow velocity is the key parameter affecting the shock vibration suppression effect: at 0 m/s flow velocity, the phonon crystal bandgap can effectively attenuate the shock response; as the flow velocity increases to 10 m/s, the fluid-solid coupling effect is enhanced, and the attenuation intensity is weakened. This study elucidates the quantitative relationship between key parameters such as flow velocity, structural periodicity, and resonant unit characteristics and shock vibration attenuation performance. It is expected to provide an important theoretical foundation and design basis for the design of flow microchannel systems with excellent shock resistance.

This paper addresses the core contradiction in fault diagnosis of gearboxes in heavy-duty equipment, where it is challenging to achieve both lightweight and robustness in dynamic industrial environments. Current diagnostic algorithms often struggle with balancing computational efficiency and diagnostic accuracy, particularly in noisy and variable operating conditions. Many existing methods either rely on complex architectures that are computationally expensive or oversimplified models that lack robustness to environmental interference. A novel, lightweight, and robust diagnostic network, YConvFormer, is proposed. Firstly, a time–frequency joint input channel is introduced, which integrates time-domain waveforms and frequency-domain spectrums at the input layer. It incorporates an Efficient Channel Attention mechanism with dynamic weighting to filter noise in specific frequency bands, suppressing high-frequency noise and enhancing the complementary relationship between time–frequency features. Secondly, an axial-enhanced broadcast attention mechanism is proposed. It models long-range temporal dependencies through spatial axial modeling, expanding the receptive field of shock features, while channel axial reinforcement strengthens the interaction of harmonics across frequency bands. This mechanism refines temporal modeling with minimal computation. Finally, the YConvFormer lightweight architecture is proposed, which combines shallow feature processing with global–local modeling, significantly reducing computational load. The experimental results on the XJTU and SEU gearbox datasets show that the proposed method improves the average accuracy by 6.55% and 19.58%, respectively, compared to the best baseline model, LiteFormer.

Schizophrenia (SZ) is a complex neuropsychiatric disorder characterized by heterogeneous symptoms, relatively poor clinical outcome, and widespread disruptions in neural connectivity and oscillatory dynamics. This article attempts to review current evidence linking genomic and proteomic alterations with aberrant neural oscillations observed in SZ, including aberrations in all oscillatory frequency bands obtained via human EEG. The numerous genes discussed are mainly involved in modulating synaptic transmission, synaptic function, interneuron excitability, and excitation/inhibition balance, thereby influencing the generation and synchronization of neural oscillations at specific frequency bands (e.g., gamma frequency band) critical for different cognitive, emotional, and perceptual processes in humans. The review highlights how polygenic influences and gene-circuit interactions underlie the neural oscillatory and connectivity abnormalities central to SZ pathophysiology, providing a framework for future research on common genetic-neural function interactions and on potential therapeutic interventions targeting local and global network-level neural dysfunction in SZ patients. As will be discussed, many of these genes affecting neural oscillations in SZ also affect other neurological disorders, ranging from autism to epilepsy. In time, it is hoped that future research will show why the same genetic anomaly leads to one illness in one person and to another illness in a different person.