Biology subjects

zhang, g.

Publications and source records attributed to zhang, g..

8 recordsLinked to original sources

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

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

Evaluating the effects of regularization and cross-validation parameters on the performance of SVM-based decoding of EEG data

Regularization has been extensively used in multivariate pattern classification (MVPA; decoding) of EEG data to mitigate the risk of overfitting. N-fold cross-validation is also used to mitigate this risk, and it is often combined with averaging across trials to improve the SNR. However, the impact of different regularization and cross-validation parameters on decoding performance remains unclear. This study aimed to evaluate the effects of variations in the support vector machine (SVM) regularization parameter (C) and the number of crossfolds (and the number of trials per average) on the performance of SVM-based decoding analyses. To achieve this, we examined the decoding performance in relatively simple binary classification tasks from seven commonly used event-related potential paradigms (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). Additionally, we evaluated the decoding performance in more challenging multiclass tasks, including decoding face identity, facial expression, stimulus location, and stimulus orientation. The results revealed that both decoding accuracy and effect size were highest when the regularization strength was equal to or greater than 1. Furthermore, using between 3 and 5 folds with at least 10 trials per average yielded optimal decoding performance in most cases. Researchers applying SVM-based decoding to datasets similar to those examined here--in terms of population, recording systems, class numbers, and paradigms--might benefit from using the parameters that we found to be optimal here.

neuroscience↗

bioRxiv2025

CDK11 activates CDK12 to trigger the elongation of RNA Polymerase II

CDK11 is an essential cyclin-dependent kinase in higher eukaryotes, yet its specific substrates and functional roles have remained elusive. Our findings reveal that CDK11 is functionally analogous to the yeast Bur1 kinase, phosphorylating the repeat region of hSpt5 and the linker domain of Rpb1 in RNA Polymerase II (Pol II). Inhibition of CDK11 results in a significant reduction of active Pol II not only at transcription start sites but also along gene bodies. Further investigation finds that CDK11 is crucial for the phosphorylation and activation of CDK12, which is essential for the elongation of Pol II. Moreover, we find that CDK10 can partially compensate for the function of CDK11. Combining with earlier functional elucidation of CDK9 from us, the consequent roles of CDK7/8, CDK9, CDK10/11, and CDK12/13 in transcription regulation in metazoans are established.

biochemistry↗

bioRxiv2025

Assessing the impact of artifact correction and artifact rejection on the performance of SVM-based decoding of EEG signals

Numerous studies have demonstrated that eyeblinks and other large artifacts can decrease the signal-to-noise ratio of EEG data, resulting in decreased statistical power for conventional univariate analyses. However, it is not clear whether eliminating these artifacts during preprocessing enhances the performance of multivariate pattern analysis (MVPA; decoding), especially given that artifact rejection reduces the number of trials available for training the decoder. This study aimed to evaluate the impact of artifact-minimization approaches on the decoding performance of support vector machines. Independent component analysis (ICA) was used to correct ocular artifacts, and artifact rejection was used to discard trials with large voltage deflections from other sources (e.g., muscle artifacts). We assessed decoding performance in relatively simple binary classification tasks using data from seven commonly-used event-related potential paradigms (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity), as well as more challenging multi-way decoding tasks, including stimulus location and stimulus orientation. The results indicated that the combination of artifact correction and rejection did not improve decoding performance in the vast majority of cases. However, artifact correction may still be essential to minimize artifact-related confounds that might artificially inflate decoding accuracy. Researchers who are decoding EEG data from paradigms, populations, and recording setups that are similar to those examined here may benefit from our recommendations to optimize decoding performance and avoid incorrect conclusions.

neuroscience↗

bioRxiv2025

A perturbation proteomics-based foundation model for virtual cell construction

Building a virtual cell requires comprehensive understanding of protein network dynamics of a cell which necessitates large-scale perturbation proteome data and intelligent computational models learned from the proteome data corpus. Here, we generate a large-scale dataset of over 38 million perturbed protein measurements in breast cancer cell lines and develop a neural ordinary differential equation-based foundation model, namely ProteinTalks. During pretraining, ProteinTalks gains a fundamental understanding of cellular protein network dynamics. Our model encodes protein networks and exhibits consistently improved predictive accuracy across various downstream tasks, highlighting its generalization capabilities and adaptability. In cancer cells, ProteinTalks robustly predicts drug efficacy and synergy, identifies novel drug combinations, and, through its interpretability, uncovers resistance-associated proteins. When applied to more complex system, patient-derived tumor xenografts, ProteinTalks predicts potential responses to drugs. Its integration with clinical patient data enhances the prognosis prediction of breast cancer patients. Collectively, we present a foundational model based on proteome dynamics, offering potential for various downstream applications, including drug discovery, and providing a basis for developing virtual cells.

systems biology↗

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↗