arXiv subjects

Shiang Hu

Publications and source records attributed to Shiang Hu.

6 recordsLinked to original sources

arXiv2025

GANORM: Lifespan Normative Modeling of EEG Network Topology based on Multinational Cross-Spectra

Charting the lifespan evolutionary trajectory of brain function serves as the normative standard for preventing mental disorders during brain development and aging. Although numerous MRI studies have mapped the structural connectome for young cohorts, the EEG-based functional connectome is unknown to characterize human lifespan, limiting its practical applications for the early detection of brain dysfunctions at the community level. This work aimed to undertake normative modeling from the perspective of EEG network topology. Frequency-dependent scalp EEG functional networks were constructed based on EEG cross-spectra aged 5-97 years from 9 countries and network characteristics were quantified. First, GAMLSS were applied to describe the normative curves of the network characteristics in different frequency bands. Subsequently, addressing the limitations of existing regression approaches for whole brain network analysis, this paper proposed an interpretable encoder-decoder framework, Generative Age-dependent brain Network nORmative Model (GANORM). Building upon this framework, we established an age-dependent normative trajectory of the complete brain network for the entire lifespan. Finally, we validated the effectiveness of the norm using EEG datasets from multiple sites. Subsequently, we evaluated the effectiveness of GANORM, and the tested performances of BPNN showed the R^2 was 0.796, the MAE was 0.081, and the RMSE was 0.013. Following established lifespan brain network norm, GANORM also exhibited good results upon verification using healthy and disease data from various sites. The deviation scores from the normative mean for the healthy control group were significantly smaller than those of the disease group.

cs.NE↗

arXiv2025

Exploring the distribution of connectivity weights in resting-state EEG networks

The resting-state brain networks (RSNs) reflects the functional connectivity patterns between brain modules, providing essential foundations for decoding intrinsic neural information within the brain. It serves as one of the primary tools for describing the spatial dynamics of the brain using various neuroimaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG). However, the distribution rules or potential modes of functional connectivity weights in the resting state remain unclear. In this context, we first start from simulation, using forward solving model to generate scalp EEG with four channel densities (19, 32, 64, 128). Subsequently, we construct scalp brain networks using five coupling measures, aiming to explore whether different channel density or coupling measures affect the distribution pattern of functional connectivity weights. Next, we quantify the distribution pattern by calculating the skewness, kurtosis, and Shannon entropy of the functional connectivity network weights. Finally, the results of the simulation were validated in a normative database. We observed that: 1) The functional connection weights exhibit a right-skewed distribution, and are not influenced by channel density or coupling measures; 2) The functional connection weights exhibit a relatively uniform distribution, with the potential for volume conduction to affect the degree of uniformity in the distribution; 3) Networks constructed using coupling measures influenced by volume conduction exhibit significant correlations between the average connection weight and measures of skewness, kurtosis, and Shannon entropy. This study contributes to a deeper understanding of RSNs, providing valuable insights for research in the field of neuroscience, and holds promise for being associated with brain cognition and disease diagnosis.

cs.HC↗

arXiv2023

How do the resting EEG preprocessing states affect the outcomes of postprocessing?

Plenty of artifact removal tools and pipelines have been developed to correct the EEG recordings and discover the values below the waveforms. Without visual inspection from the experts, it is susceptible to derive improper preprocessing states, like the insufficient preprocessed EEG (IPE), and the excessive preprocessed EEG (EPE). However, little is known about the impacts of IPE or EPE on the postprocessing in the frequency, spatial and temporal domains, particularly as to the spectra and the functional connectivity (FC) analysis. Here, the clean EEG (CE) was synthesized as the ground truth based on the New-York head model and the multivariate autoregressive model. Later, the IPE and the EPE were simulated by injecting the Gaussian noise and losing the brain activities, respectively. Then, the impacts on postprocessing were quantified by the deviation caused by the IPE or EPE from the CE as to the 4 temporal statistics, the multichannel power, the cross spectra, the dispersion of source imaging, and the properties of scalp EEG network. Lastly, the association analysis was performed between the PaLOSi metric and the varying trends of postprocessing with the evolution of preprocessing states. This study shed light on how the postprocessing outcomes are affected by the preprocessing states and PaLOSi may be a potential effective quality metric.

q-bio.NC↗

arXiv2023

Spectral homogeneity cross frequencies can be a quality metric for the large-scale resting EEG preprocessing

The brain projects require the collection of massive electrophysiological data, aiming to the longitudinal, sectional, or populational neuroscience studies. Quality metrics automatically label the data after centralized preprocessing. However, although the waveforms-based metrics are partially useful, they may be unreliable by neglecting the spectral profiles. Here, we detected the phenomenon of parallel log spectra (PaLOS) that the scalp EEG power in the log scale were parallel to each other from 10% of 2549 HBN EEG. This phenomenon was reproduced in 8% of 412 PMDT EEG from 4 databases. We designed the PaLOS index (PaLOSi) to indicate this phenomenon by decomposing the cross-spectra at different frequencies into the common principal component spaces. We found that the PaLOS biophysically implied a prominently dominant dipole in the source space which was implausible for the resting EEG. And it may be practically resulted from excessive preprocessing. Compared with the 1966 normative EEG cross-spectra, the HBN and the PMDT EEG with PaLOS presented generally much higher electrode pairwise coherences and higher similarity of coherence-based network patterns, which went against the known frequency dependent characteristic of coherence networks. We suggest the PaLOSi should lay in the range of 0.4-0.7 for large resting EEG quality assurance.

cs.HC↗

arXiv2018

Restate the reference for EEG microstate analysis

Despite the decades of efforts, the choice of EEG reference is still a debated fundamental issue. Non-neutral reference can inevitably inject the uncontrolled temporal biases into all EEG recordings, which may influence the spatiotemporal analysis of brain activity. A method, termed microstates, identifying spatiotemporal EEG features as the quasi-stable topography states in milliseconds, suggests its potential as biomarkers of neurophysiological disease. As reference electrode standardization technique (REST) could reconstruct an infinity reference approximately, it is a question whether REST or the other references will be more reliable than average reference (AR) for the microstates analysis. In this study, we design the microstate-based EEG forward model, and apply different references for microstates analysis. The spatial similarity between the generated and assumed cluster maps is mainly investigated. Furthermore, the real EEG data by the parametric bootstrap method is used to validate the performance of the references. Finally, we find that REST is robust to recover more similar cluster maps to the assumption than AR in the simulation, and the cluster maps between REST and AR on the real EEG data are quite different. This study may indicate that REST contributes to identifying more objective microstates features than AR.

q-bio.QM↗

arXiv2018

Unified Bayesian estimator of EEG reference at infinity: rREST

The choice of reference for electroencephalogram (EEG) is a long-lasting unsolved issue resulting in inconsistent usages and endless debates. Currently, both average reference (AR) and reference electrode standardization technique (REST) are two primary, irreconcilable contenders. We propose a theoretical framework to resolve this issue by formulating both a) estimation of potentials at infinity, and, b) determination of the reference, as a unified Bayesian linear inverse problem. We find that AR and REST are very particular cases of this unified framework: AR results from biophysically non-informative prior; while REST utilizes the prior of EEG generative model. We develop the regularized versions of AR and REST, named rAR, and rREST, respectively. Both depend on a regularization parameter that is the noise to signal ratio. Traditional and new estimators are evaluated with this framework, by both simulations and analysis of real EEGs. Generated artificial EEGs, show that relative error in estimating the EEG potentials at infinity is lowest for rREST. It also reveals that realistic volume conductor models improve the performances of REST and rREST. For practical applications, it is shown that average lead field gives the results comparable to the individual lead field. Finally, it is shown that the selection of the regularization parameter with Generalized Cross-Validation (GCV) is close to the 'oracle' choice based on the ground truth. When evaluated with the real 89 resting state EEGs, rREST consistently yields the lowest GCV. This study provides a novel perspective on the EEG reference problem by means of a unified inverse solution framework. It may allow additional principled theoretical formulations and numerical evaluation of performance.

q-bio.QM↗