Biology subjects

Cong, F.

Publications and source records attributed to Cong, F..

At least 19 recordsLinked to original sources

bioRxiv2026

TLS-Sim: An Open-Source Toolkit for Individualized Transcranial Light Stimulation Modeling with Deep-Learning-Accelerated Simulation

Background: Accurate modeling of photon transport through the heterogeneous tissues of the human head is important for individualized transcranial light stimulation (tLS). However, high-precision Monte Carlo (MC) simulations can be computationally demanding, and existing general-purpose simulation tools do not provide an integrated, brain-oriented workflow. Methods: We developed TLS-Sim, a framework that integrates subject-specific head-model construction, multiple light-source configuration, GPU-accelerated MC photon simulation, visualization, and intracranial dosimetric analysis. Its central feature is a deep-learning-accelerated MC pathway, termed Flux-to-Flux, that estimates a high-photon-count energy-deposition field from a paired low-photon-count simulation. Sparse and full Monte Carlo fields were encoded using a three-dimensional variational autoencoder (VAE), and a three-dimensional velocity network was trained to transform the sparse latent representation toward the corresponding full-simulation representation. Results: We demonstrate the end-to-end toolkit and the Flux-to-Flux (F2F) pathway on a representative individualized head model evaluated across three source configurations. Relative to the sparse simulation, the learned prediction improved agreement with the full Monte Carlo reference by approximately +4 dB in signal-to-noise ratio on average, with correspondingly higher structural similarity and lower mean absolute error. Conclusions: TLS-Sim provides a modular, openly released computational framework for individualized tLS simulation and analysis. The Flux-to-Flux pathway is presented as an implemented capability that reduces the computational burden of high-photon-count simulation while improving spatial agreement with the full MC reference, most strongly at the low-fluence field boundaries where the sparse simulation is noisiest.

bioengineering↗

bioRxiv2026

Shared but temporally distinct neural representations support semantic matching across word and picture formats: evidence from EEG decoding and temporal generalization analyses

Audiovisual semantic matching can be achieved using either written words or pictures, yet whether these formats engage shared semantic matching representations with similar temporal dynamics remains unclear. We recorded electroencephalography from 27 participants while they performed audiovisual semantic matching tasks in which spoken words were paired with either written words or pictures. Stimuli included both natural and man-made objects. Time-resolved multivariate pattern analyses (MVPA or decoding), cross-decoding, and temporal generalization analyses were used to characterize the temporal dynamics of semantic processing. Reliable decoding of matching versus mismatching judgments emerged in both word and picture conditions. Decoding onset that significant above chance level occurred earlier for written words than for pictures and cross-decoding analyses revealed successful generalization between word and picture formats. Temporal generalization analyses further demonstrated distinct representational dynamics across formats, with word processing characterized by predominantly time-specific neural representations and picture processing showing more sustained and temporally stable representations. In addition, matching-related discrimination emerged earlier for natural objects than for man-made objects across both formats. The results suggest that speech-word matching shows earlier neural evidence of audiovisual alignment than speech-picture matching, potentially reflecting differences in how auditory linguistic input is integrated with visual information across representational formats.

neuroscience↗

bioRxiv2026

Searchlight Optimization Using Representational Similarity Analysis for Subject-Level Voxel Selection in Emotional State Decoding

Identifying informative voxels is a critical, yet challenging step in functional magnetic resonance imaging (fMRI), particularly for multivariate analyses involving multiple related conditions. Existing approaches often rely on predefined regions of interest (ROIs) or activation-based criteria, which may be insufficient for capturing fine-grained representational differences. This challenge becomes particularly relevant in experimental settings and interventions such as neurofeedback training, where voxels are not only measured as neural responses but also used as targets for intervention based on their previously observed activity patterns. In this study, we propose a subject-level searchlight optimization framework that integrates voxel-wise general linear model (GLM)-based univariate analysis with representational similarity analysis (RSA)-based multivariate refinement to identify voxels that are both task-relevant and condition-sensitive. To enhance practical applicability, the framework further incorporates a data-driven hyperparameter tuning step based on Bayesian optimization, enabling efficient identification of high-performing configurations from small pilot datasets, with consistent performance when applied to larger samples. The proposed framework was evaluated using an emotion imagery fMRI dataset with four affective conditions. Results demonstrate that the multivariate refinement improves alignment between empirical and target representational structures compared with univariate selection alone. Compared with a classifier-based voxel selection approach, the RSA-based approach better preserves the representational geometry of emotional states while maintaining discriminative capacity. These findings highlight the effectiveness, efficiency, and robustness of the proposed RSA framework, providing a practical solution for identifying condition-sensitive voxels and supporting more precise multivariate investigation of affective brain states in multi-condition fMRI studies.

neuroscience↗

bioRxiv2026

Tumor-induced species-specific dysbiosis drives renal innate immunity and nephrogenic ascites

Ascites is a life-threatening complication of advanced malignancies, yet how tumors disrupt systemic fluid homeostasis remains poorly understood. Here, using a Drosophila tumor allograft model that recapitulates key features of cancer-associated ascites, we identify a tumor-microbiome-renal axis that controls host fluid balance. Tumor-bearing hosts develop severe abdominal fluid accumulation accompanied by marked expansion and systemic dissemination of the gut commensal Acetobacter aceti. Tumor-induced bacterial dissemination activates innate immune signaling in the Malpighian tubules, the insect renal tubules, leading to uric acid accumulation, nephrolithiasis, and progressive ascites. Selective elimination of A. aceti, or renal-tubule-specific suppression of IMD/NF-{kappa}B signaling, abolishes these pathological changes. Conversely, mono-association of axenic hosts with A. aceti is sufficient to recapitulate the ascites phenotype through IMD pathway activation. Together, these findings demonstrate that tumors can remotely induce nephrogenic pathology through species-specific microbiome-dependent immune activation, establishing a mechanistic link between cancer progression and systemic fluid imbalance.

cancer biology↗

bioRxiv2026

Task-Dependent Effects of Channel Density on Multivariate Pattern Analysis and Spatial Representational Similarity Analysis: Evidence from Three EEG Datasets

Despite the widespread use of EEG decoding and representational similarity analysis (RSA), the extent of the impact of electrode density on these multivariate approaches has not been systematically evaluated, particularly across different cognitive domains. Here, we systematically compared four electrode configurations (19, 32, 64, and 128 channels) across three EEG datasets encompassing visual perception, visual search, and emotional processing. All datasets were recorded at 128 channels and subsequently reduced to 64, 32, and 19 channels via electrode subset selection based on the international 10-20 system. Time-resolved decoding and spatial RSA were computed for each configuration with paired statistical evaluation. Decoding sensitivity demonstrated clear task dependence: in the color-harmony paradigm, performance improved markedly with higher densities, consistent with spatially distributed perceptual representations, whereas decoding in the icon-search and dot-probe tasks remained stable across densities, indicating diminishing returns of additional electrodes although all configuration yielded robust decoding accuracies that above chance level. RSA results were comparatively robust, with relative representational similarity structures largely preserved even for 16 channel counts. These findings demonstrate that low-density setups can be sufficient, offering practical guidance for EEG channel selection and supporting the use of low-density EEG in real-world applications, including wearable systems, mobile EEG recordings, and clinical or rehabilitation settings.

neuroscience↗

bioRxiv2025

The impact of downsampling on data quality, univariate measurement and multivariate pattern analysis in event-related potential research

The choice of sampling rate is a critical preprocessing step in event-related potential (ERP) research, yet its impact on different analytic approaches remains underexplored. In this study, we systematically evaluated how downsampling affects data quality measured via Standardized Measurement Error (SME), conventional univariate ERP metrics (mean amplitude, peak amplitude, peak latency, and 50% area latency), and multivariate pattern analysis (MVPA; decoding). We analyzed seven commonly studied ERP components: P3, N400, N170, N2pc, mismatch negativity, error-related negativity, and lateralized readiness potential collected from neurotypical young adults. Results showed that both amplitude- and latency-based scores were significantly affected by sampling rate changes, particularly for 50% area latency that presented increased SME (reduced data quality) at lower sampling rates. Amplitude-based measures and their corresponding effect sizes were more influenced by sampling rate variations, whereas latency-based measures were comparatively stable across different temporal resolutions. In contrast, multivariate decoding performance remained highly robust, with decoding accuracy and effect sizes showing minimal variation even at the lowest sampling rate (e.g., 64 Hz). Overall, these findings suggest that caution should be exercised when employing lower sampling rates for data quality and conventional univariate ERP analyses. Nevertheless, lower sampling rates (e.g., 64 Hz) may still be appropriate for decoding analyses, particularly in studies where precise temporal resolution is not critical. For researchers analyzing ERP data with similar components, noise levels, and participant populations as in this study, following these recommendations should yield robust statistical power.

neuroscience↗

bioRxiv2025

EEG-based analyses reveal different temporal processing patterns in aesthetic evaluation

BackgroundExisting studies estimate harmony values using the color harmony model, but how these values are reflected in distinct neural stages (early automatic vs. late evaluative) and shape subjective preferences remains unexamined. MethodsTo fill this gap, we recorded behavioral and event-related potential data (N=30 adults, age: 29.20{+/-}2.38 years old) to examine whether the predictions of color harmony theory regarding harmonious vs. disharmonious color combinations align with neural activity from a color perception paradigm. ResultsBehavioral results showed that two-color combinations received higher aesthetic pleasantness ratings than three-color combinations, while the ratings for color harmony were relatively smaller than for disharmony. Univariate ERP analysis revealed that P1 amplitude for harmonious color was significantly higher than for disharmonious color, particularly when three colors were present. In contrast, the amplitude and latency of P2 were significantly impacted by the number of colors. Additionally, multivariate pattern analysis further demonstrated that neural activity reliably differentiated between harmonious vs. disharmonious color combinations and two-color vs. three-color combinations during both P1 and P2 time window. ConclusionsThese findings indicate that harmonious versus disharmonious color combinations subtly influenced early visual processing, while the number of colors used in the design had a stronger influence on aesthetic judgments.

neuroscience↗

bioRxiv2025

Toward navigating emotional states using real-time representational similarity analysis fMRI neurofeedback - a feasibility study

Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) is a promising non-invasive brain-computer-interface (BCI) technique for enhancing self-regulation of affective states in the brain. However, conventional univariate rt-fMRI-NF approaches are limited in their ability to distinguish neural patterns of distinct emotions that involve overlapping brain regions. In this study, we applied an rt-fMRI semantic neurofeedback (rt-fMRI-sNF) paradigm, incorporating real-time representational similarity analysis (rt-RSA) to enable navigation between emotional states. Four emotional patterns were first derived from functional localizer runs, each designed to evoke a specific emotion, and then applied as target patterns during neurofeedback. Using an RSA-informed circular semantic map (CSM), participants received real-time visual feedback indicating both the similarity and intensity of their current brain activity relative to target emotional patterns. Participants were instructed to use mental imagery to shift their brain activity toward the specific target pattern and increase its intensity. Twenty-four healthy participants completed the localizer runs, and two consecutive neurofeedback runs in the same session. Ten participants successfully engaged with both the similarity and intensity components of the CSM, showing effective modulations of their mental states. Analyses of the localizer runs revealed overlapping regional activations across emotions and demonstrated that RSA outperformed univariate analysis in distinguishing between them. For the neurofeedback runs, linear mixed-effects model (LMM) analyses across multiple performance metrics indicated consistent within-run improvements and higher initial performance in the second run, while significant between-run learning effects emerged only in exploratory models with quadratic time terms. A block-wise comparison also showed significantly higher performance at the end of each run compared to the beginning based on the intensity metric. These findings support the usability of RSA in differentiating multiple emotional states and demonstrate the feasibility of the rt-fMRI-sNF paradigm for emotion regulation.

neuroscience↗

bioRxiv2023

Septoria tritici blotch resistance gene Stb15 encodes a lectin receptor-like kinase

Septoria tritici blotch (STB), caused by the Dothideomycete fungus Zymoseptoria tritici, is of one of the most damaging diseases of bread wheat (Triticum aestivum)1 and the target of costly fungicide applications2. In line with the fungus apoplastic lifestyle, STB resistance genes isolated to date encode receptor-like kinases (RLKs) including a wall-associated kinase (Stb6) and a cysteine-rich kinase (Stb16q)3,4. Here, we used genome-wide association studies (GWAS) on a panel of 300 whole-genome shotgun-sequenced diverse wheat landraces (WatSeq consortium) to identify a 99 kb region containing six candidates for the Stb15 resistance gene. Mutagenesis and transgenesis confirmed a gene encoding an intronless G-type lectin RLK (LecRK) as Stb15. The characterisation of Stb15 exemplifies the unexpected diversity of RLKs conferring Z. tritici resistance in wheat.

genetics↗

bioRxiv2023

Site effects depth denoising and signal enhancement using dual-projection based ICA model

Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realize the full advantages of pooling multi-site datasets. Independent component analysis (ICA) and general linear model (GLM) based denoising methods are the two primary methods used to denoise scanner/site-related effects. Unfortunately, there are challenges with both ICA-based and GLM-based denoising methods to remove site effects completely when the signals of interest and scanner/site-related noises are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful denoising strategy that implements dual-projection (DP) theory based on ICA to remove the scanner/site-related effects more completely. This method can separate the signal effects correlated with noise variables from the identified noise effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a traveling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the proposed GLM- and ICA-based denoising methods and our DP-based ICA denoising method. Results show that DP-based ICA

neuroscience↗

bioRxiv2022

Cell polarity opposes Jak-STAT mediated Escargot activation that drives intratumor heterogeneity in a Drosophila tumor model

In proliferating neoplasms, microenvironment-derived selective pressures promote tumor heterogeneity by imparting diverse capacities for growth, differentiation and invasion. However, what makes a tumor cell respond to signaling cues differently from a normal cell is not well understood. In the Drosophila ovarian follicle cells, apicobasal-polarity loss induces heterogenous epithelial multilayering. When exacerbated by oncogenic-Notch expression, this multilayer displays an increased consistency in the occurrence of morphologically distinguishable cells adjacent to the polar follicle cells. Polar cells release the Jak-STAT ligand Unpaired (Upd), in response to which, neighboring polarity-deficient cells exhibit a precursor-like transcriptomic state. Using single-cell transcriptomics, we discovered the ectopic activation of the Snail-family transcription factor Escargot (Esg) in these cells. We also characterized similar relationship between Upd and Esg during early follicular development, where the establishment of polarity determines follicle-cell differentiation. Overall, our results indicate that epithelial-cell polarity acts as a gatekeeper against microenvironmental selective pressures that drive heterogeneity.

cancer biology↗

bioRxiv2021

The adenosine analogue prodrug ATV006 is orally bioavailable and has potent preclinical efficacy against SARS-CoV-2 and its variants

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the COVID-19 pandemic, is rapidly evolving. Due to the limited efficacy of vaccination in prevention of SARS-CoV-2 transmission and continuous emergence of variants of concern (VOC), including the currently most prevalent Delta variant, orally bioavailable and broadly efficacious antiviral drugs are urgently needed. Previously we showed that adenosine analogue 69-0 (also known as GS-441524), possesses potent anti-SARS-CoV-2 activity. Herein, we report that esterification of the 5-hydroxyl moieties of 69-0 markedly improved the antiviral potency. The 5-hydroxyl-isobutyryl prodrug, ATV006, showed excellent oral bioavailability in rats and cynomolgus monkeys and potent antiviral efficacy against different VOCs of SARS-CoV-2 in cell culture and three mouse models. Oral administration of ATV006 significantly reduced viral loads, alleviated lung damage and rescued mice from death in the K18-hACE2 mouse model challenged with the Delta variant. Moreover, ATV006 showed broad antiviral efficacy against different mammal-infecting coronaviruses. These indicate that ATV006 represents a promising oral drug candidate against SARS-CoV-2 VOCs and other coronaviruses.

microbiology↗

bioRxiv2021

Identifying Oscillatory Hyperconnectivity andHypoconnectivity Networks in Major DepressionUsing Coupled Tensor Decomposition

Previous researches demonstrate that major depression disorder (MDD) is associated with widespread network dysconnectivity, and the dynamics of functional connectivity networks are important to delineate the neural mechanisms of MDD. Cortical electroencephalography (EEG) oscillations act as coordinators to connect different brain regions, and various assemblies of oscillations can form different networks to support different cognitive tasks. Studies have demonstrated that the dysconnectivity of EEG oscillatory networks is related with MDD. In this study, we investigated the oscillatory hyperconnectivity and hypoconnectivity networks in MDD under a naturalistic and continuous stimuli condition of music listening. With the assumption that the healthy group and the MDD group share similar brain topology from the same stimuli and also retain individual brain topology for group differences, we applied the coupled nonnegative tensor decomposition algorithm on two adjacency tensors with the dimension of time x frequency x connectivity x subject, and imposed double-coupled constraints on spatial and spectral modes. The music-induced oscillatory networks were identified by a correlation analysis approach based on the permutation test between extracted temporal factors and musical features. We obtained three hyperconnectivity networks from the individual features of MDD and three hypoconnectivity networks from common features. The results demonstrated that the dysfunction of oscillation-modulated networks could affect the involvement in music perception for MDD patients. Those oscillatory dysconnectivity networks may provide promising references to reveal the pathoconnectomics of MDD and potential biomarkers for the diagnosis of MDD.

neuroscience↗

bioRxiv2021

Single-trial-based Temporal Principal Component Analysis on Extracting Event-related Potentials of Interest for an Individual Subject

Temporal principal component analysis (t-PCA) has been widely used to extract event-related potentials (ERPs) at the group level of multiple subjects ERP data. The t-PCA is, however, poorly employed to isolate ERPs from single-trial data of an individual subject. Additionally, the effects of varied trial numbers on the yields of t-PCA have not been systematically examined. To fill both gaps, in an emotional experiment (22 subjects), we use t-PCA and Promax rotation to extract interesting P2/N2 from single-trial data of each subject with consecutive increasingly trial numbers (from 10 to 42) and all trials, respectively. Besides, time-domain analysis and other two group t-PCA strategies (trial-averaged and single-trial) are also employed to isolate ERPs of interest from all subjects. The results indicate that the proposed technique produces the internal consistent measure of N2 from few trials (i.e., 19) as from all trials compared with the other three approaches (more than 30 trials). As for P2, all approaches yield internal-subject consistent effect after approximately 33 trials are included in the average, but Cronbachs alpha values for the proposed technique are higher than the other two group PCA strategies over varied trials. Combined, the yields provide evidence that the proposed approach may efficiently temporally filter the data to extract more reliable and stable ERPs for an individual subject.

neuroscience↗

bioRxiv2021

Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data

The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. Intersubject correlation (ISC) analysis of functional magnetic resonance imaging (fMRI) data is a widely used method that can measure neural responses to naturalistic stimuli that are consistent across subjects. However, interdependent correlation values in ISC artificially inflated the degrees of freedom, which hinders the investigation of individual differences. Besides, the existing ISC model mainly focus on similarities between subjects but fails to distinguish neural responses to different stimuli features. To estimate large-scale brain networks evoked with naturalistic stimuli, we propose a novel analytic framework to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In the framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. Tensor component analysis (TCA) will then reveal spatially and temporally shared components, i.e., naturalistic stimuli evoked networks, their temporal courses of activity and subject loadings of each component. To enhance the reproducibility of the estimation with TCA, a novel spectral clustering method, tensor spectral clustering, was proposed and applied to evaluate the stability of TCA algorithm. We demonstrate the effectiveness of the proposed framework via simulations and real fMRI data collected during a motor task with a traditional fMRI study design. We also apply the proposed framework to fMRI data collected during passive movie watching to illustrate how reproducible brain networks are identified evoked by naturalistic movie viewing.

neuroscience↗

bioRxiv2020

Remdesivir Metabolite GS-441524 Efficiently Inhibits SARS-CoV-2 Infection in Mouse Model

The outbreak of coronavirus disease 2019 (COVID-19) rapidly spreads across worldwide and becomes a global pandemic. Remdesivir is the only COVID-19 treatment approved by U.S. Food and Drug Administration (FDA); however, its effectiveness is still under questioning as raised by the results of a large WHO Solidarity Trial. Herein, we report that the parent nucleotide of remdesivir, GS-441524, potently inhibits the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Vero E6 and other cells. It exhibits good plasma distribution and longer half-life (t1/2=4.8h) in rat PK study. GS-441524 is highly efficacious against SARS-CoV-2 in AAV-hACE2 transduced mice and murine hepatitis virus (MHV) in mice, reducing the viral titers in CoV-attacked organs, without noticeable toxicity. Given that GS-441524 was the predominant metabolite of remdesivir in the plasma, the anti-COVID-19 effect of remdesivir may partly come from the effect of GS-441524. Our results also supported that GS-441524 as a promising and inexpensive drug candidate in the treatment of COVID-19 and future emerging CoVs diseases.

pharmacology and toxicology↗

bioRxiv2020

SingleChannelNet: A model for Automatic Sleep Stage Classification with Raw Single-Channel EEG

In diagnosing sleep disorders, sleep stage classification is a very essential yet time-consuming process. Most of the existing state-of-the-art approaches rely on hand-crafted features and multi-modality polysomnography (PSG) data, where prior knowledge is compulsory and high computation cost can be expected. Besides, few studies are able to obtain high accuracy sleep staging using raw single-channel electroencephalogram (EEG). To overcome these shortcomings, this paper proposes an end-to-end framework with a deep neural network, namely SingleChannelNet, for automatic sleep stage classification based on raw single-channel EEG. The proposed model utilizes a 90s epoch as the textual input and employs two multi-convolution blocks and several max-average pooling layers to learn different scales of feature representations. To demonstrate the efficiency of the proposed model, we evaluate our model using different raw single-channel EEGs (C4/A1 and Fpz-Cz) on two different datasets (CC-SHS and Sleep-EDF datasets). Experimental results show that the proposed architecture can achieve better over-all accuracy and Cohens kappa (CCSHS: 90.2%-86.5%, Sleep-EDF: 86.1%-80.5%) compared with state-of-the-art approaches. Additionally, the proposed model can learn features automatically for sleep stage classification using different single-channel EEGs with distinct sampling rates from different datasets without using any hand-engineered features.

bioengineering↗

bioRxiv2020

Objective Extraction of Evoked Event-related Oscillations from Time-frequency Representation of Event-related Potentials

Evoked event-related oscillations (EROs) have been widely used to explore the mechanisms of brain activities for both normal people and neuropsychiatric disease patients. The selection of regions of evoked EROs tends to be subjectively based on the previous studies and the visual inspection of grand averaged time-frequency representations (TFRs) which causes some missing or redundant information. Meanwhile, the evoked EROs cannot be fully extracted via the conventional time-frequency analysis (TFA) method because they are sometimes overlapped with each other or with artifacts in time, frequency, and space domains to some extent. Hence, these shortcomings may pose some challenges to investigate the related neuronal processes. A data-driven approach was introduced to fill the gaps as below: extracting the temporal and spatial components of interest simultaneously by principal component analysis and Promax rotation and projecting them to the electrode field to correct their variance and polarity indeterminacy, calculating the TFRs of the back-projected components, and determining the regions of interest objectively using the edge detection algorithm. We performed this novel approach and the conventional TFA method in analyzing both a synthetic dataset and an actual ERP dataset in a two-factor simple gambling paradigm of waiting time (short/long) and feedback (loss/gain) separately. Synthetic dataset results indicated that N2-theta and P3-delta oscillations were detected using the proposed approach, but, by comparison, only one oscillation was obtained via the conventional TFA method. Furthermore, the actual ERP dataset results of P3-delta for our approach revealed that it was sensitive to the waiting time (which also was found in the previous reports) but not for that of the conventional TFA method. This study manifested that the proposed approach can objectively extract evoked EROs, which allows a better understanding of the modulations of the oscillatory responses.

neuroscience↗