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Yang Xia

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4 papers

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YNIMG Journal 2021 Journal Article

Computational exploration of dynamic mechanisms of steady state visual evoked potentials at the whole brain level

Ge Zhang
Yan Cui
Yangsong Zhang
Hefei Cao
Guanyu Zhou
Haifeng Shu
Dezhong Yao
Yang Xia

Periodic visual stimulation can induce stable steady-state visual evoked potentials (SSVEPs) distributed in multiple brain regions and has potential applications in both neural engineering and cognitive neuroscience. However, the underlying dynamic mechanisms of SSVEPs at the whole-brain level are still not completely understood. Here, we addressed this issue by simulating the rich dynamics of SSVEPs with a large-scale brain model designed with constraints of neuroimaging data acquired from the human brain. By eliciting activity of the occipital areas using an external periodic stimulus, our model was capable of replicating both the spatial distributions and response features of SSVEPs that were observed in experiments. In particular, we confirmed that alpha-band (8–12 Hz) stimulation could evoke stronger SSVEP responses; this frequency sensitivity was due to nonlinear entrainment and resonance, and could be modulated by endogenous factors in the brain. Interestingly, the stimulus-evoked brain networks also exhibited significant superiority in topological properties near this frequency-sensitivity range, and stronger SSVEP responses were demonstrated to be supported by more efficient functional connectivity at the neural activity level. These findings not only provide insights into the mechanistic understanding of SSVEPs at the whole-brain level but also indicate a bright future for large-scale brain modeling in characterizing the complicated dynamics and functions of the brain.

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YNIMG Journal 2021 Journal Article

State-independent and state-dependent patterns in the rat default mode network

Wei Jing
Yang Xia
Min Li
Yan Cui
Mingming Chen
Miaomiao Xue
Daqing Guo
Bharat B. Biswal

Resting-state studies have typically assumed constant functional connectivity (FC) between brain regions, and these parameters of interest provide meaningful descriptions of the functional organization of the brain. A number of studies have recently provided evidence pointing to dynamic FC fluctuations in the resting brain, especially in higher-order regions such as the default mode network (DMN). The neural activities underlying dynamic FC remain poorly understood. Here, we recorded electrophysiological signals from DMN regions in freely behaving rats. The dynamic FCs between signals within the DMN were estimated by the phase locking value (PLV) method with sliding time windows across vigilance states [quiet wakefulness (QW) and slow-wave and rapid eye movement sleep (SWS and REMS)]. Factor analysis was then performed to reveal the hidden patterns within the DMN. We identified distinct spatial FC patterns according to the similarities between their temporal dynamics. Interestingly, some of these patterns were vigilance state-dependent, while others were independent across states. The temporal contributions of these patterns fluctuated over time, and their interactive relationships were different across vigilance states. These spatial patterns with dynamic temporal contributions and combinations may offer a flexible framework for efficiently integrating information to support cognition and behavior. These findings provide novel insights into the dynamic functional organization of the rat DMN.

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YNIMG Journal 2014 Journal Article

Simultaneous EEG-fMRI: Trial level spatio-temporal fusion for hierarchically reliable information discovery

Li Dong
Diankun Gong
Pedro A. Valdes-Sosa
Yang Xia
Cheng Luo
Peng Xu
Dezhong Yao

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have been pursued in an effort to integrate complementary noninvasive information on brain activity. The primary goal involves better information discovery of the event-related neural activations at a spatial region of the BOLD fluctuation with the temporal resolution of the electrical signal. Many techniques and algorithms have been developed to integrate EEGs and fMRIs; however, the relative reliability of the integrated information is unclear. In this work, we propose a hierarchical framework to ensure the relative reliability of the integrated results and attempt to understand brain activation using this hierarchical ideal. First, spatial Independent Component Analysis (ICA) of fMRI and temporal ICA of EEG were performed to extract features at the trial level. Second, the maximal information coefficient (MIC) was adopted to temporally match them across the modalities for both linear and non-linear associations. Third, fMRI-constrained EEG source imaging was utilized to spatially match components across modalities. The simultaneously occurring events in the above two match steps provided EEG-fMRI spatial–temporal reliable integrated information, resulting in the most reliable components with high spatial and temporal resolution information. The other components discovered in the second or third steps provided second-level complementary information for flexible and cautious explanations. This paper contains two simulations and an example of real data, and the results indicate that the framework is a feasible approach to reveal cognitive processing in the human brain.

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IJCAI Conference 2013 Conference Paper

SCMF: Sparse Covariance Matrix Factorization for Collaborative Filtering

Jianping Shi
Naiyan Wang
Yang Xia
Dit-Yan Yeung
Irwin King
Jiaya Jia

Matrix factorization (MF) is a popular collaborative ﬁltering approach for recommender systems due to its simplicity and effectiveness. Existing MF methods either assume that all latent features are uncorrelated or assume that all are correlated. To address the important issue of what structure should be imposed on the features, we investigate the covariance matrix of the latent features learned from real data. Based on the ﬁndings, we propose an MF model with a sparse covariance prior which favors a sparse yet non-diagonal covariance matrix. Not only can this reﬂect the semantics more faithfully, but imposing sparsity can also have a side effect of preventing overﬁtting. Starting from a probabilistic generative model with a sparse covariance prior, we formulate the model inference problem as a maximum a posteriori (MAP) estimation problem. The optimization procedure makes use of stochastic gradient descent and majorizationminimization. For empirical validation, we conduct experiments using the MovieLens and Netﬂix datasets to compare the proposed method with two strong baselines which use different priors. Experimental results show that our sparse covariance prior can lead to performance improvement.

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