Xiaolin Ning

JBHI Journal 2026 Journal Article

GCL-MSE: Graph Contrastive Learning with Mutual Similarity Enhancement for Drug Repositioning

• Shasha Tao
• Jin Liu
• Min Xiang
• Xin Ma
• Tongtong Huo
• Xiaolin Ning

Amidst the shift to data-driven drug repositioning, existing models struggle to capture complex semantic and topological relationships in biomedical knowledge graphs for drug-disease association (DDA) mining. We propose a Graph Contrastive Learning with Mutual Similarity Enhancement (GCL-MSE). The core innovation lies in defining a concept of mutual similarity. This concept comprises two aspects: drug therapeutic domain similarity, which captures the functional associations between drugs based on their therapeutic spectra, and disease pharmacological response similarity, which reflects the pathological associations between diseases based on drug response patterns. Based on this concept, a Mutual Similarity Enhancement mechanism (MSE) is constructed to fuse four similarities, to build a semantic relationship topology that captures the complex semantic dependencies in DDA. Further, an Adaptive Orthogonal Noise Contrastive Estimation Loss (AdaOrthoNCE) is proposed to disentangle biological relationships in the latent space while optimizing discriminative representations. GCL MSE integrates the semantic topology via MSE, employs a three-channel graph convolutional model to generate topology-semantic co-representations, and utilizes AdaOrthoNCE to learn optimized embeddings, ultimately enabling cross-scale DDA prediction. Experimental results demonstrate that GCL-MSE significantly outperforms state of-the-art models in both AUROC and AUPRC metrics, with improvements of over 4. 8% in AUROC and 25. 5% in AUPRC, thereby validating the effectiveness of collaborative modeling that integrates features from pharmacological, therapeutic, and topological perspectives. Additionally, GCL MSE predicts the therapeutic roles of drugs such as lamotrigine for Alzheimer's disease and hydroxyurea for breast cancer. Molecular docking experiments and related studies further confirm its validity.

YNIMG Journal 2026 Journal Article

VSSI-TBM: A variational sparse source imaging method based on time basis matrix

• Tianyu Gao
• Jin Ding
• Wen Li
• Fulong Wang
• Yujie Ma
• Ruonan Wang
• Yang Gao
• Xiaolin Ning

Source imaging algorithms have been widely used to localize functional and lesion areas. Brain source reconstruction is limited by complex experimental environments (noise interference, distributed brain activity, acquisition systems, etc.), and range estimation is not accurate. This study proposes a variational sparse source imaging method based on the time basis matrix (VSSI-TBM) algorithm. VSSI-TBM permits the source spatial signal to consist of several temporal basis functions by using low-rank decomposition to extract effective signals. In a compressed space, mixed-norm constraints and a cortical source variation operator ensure spatial sparsity and smoothness. In clinical examinations or research, other a priori information regarding brain activity may be available. VSSI-TBM using lead field guide constraints can further enhance the reconstruction results. The simulation results demonstrate the robust performance of VSSI-TBM in environments with a low signal-to-noise ratio (SNR), large sources ( > 11 cm 2 ), and multiple sources. Additionally, integrating prior information enhances the imaging performance in complex environments. The algorithm is evaluated using an open-source dataset and an optically pumped magnetometer-based magnetoencephalography (OPM-MEG) system with a noisy 30-channel uniform layout. The results reveal a strong robustness of the spatial range reconstruction. Moreover, the combination of prior information effectively improves the imaging performance of the OPM-MEG system.

YNIMG Journal 2025 Journal Article

Corrigendum to “Extended homogeneous field correction method based on oblique projection in OPM-MEG” [NeuroImage 306(2025) 120991]

• Fulong Wang
• Fuzhi Cao
• Yujie Ma
• Ruochen Zhao
• Ruonan Wang
• Nan An
• Min Xiang
• Dawei Wang

JBHI Journal 2025 Journal Article

Detection of Brain Mid-Sagittal Plane Based on Progressive Semi-Supervised Pixel Classification Algorithm

• Xingwen Fu
• Jinjian Xu
• Qiuyu Han
• Tianchen Wu
• Yuan Wei
• Xiaolin Ning

Automatic detection of mid-sagittal plane (MSP) in the brain is widely used for symmetry analysis, midline shift (MLS) measurement, tilt correction, and brain morphometry. Existing MSP detection methods typically employ fully supervised learning (SL). However, this approach is greatly constrained by the quantity and quality of expertly annotated data. Due to the high cost of annotation, existing methods can only use a limited number of samples with a single type for model training, resulting in low generalization of such models. We propose an MSP detection framework to improve the model's generalization performance across various types of data. Specifically, on one hand, we design a Progressive Semi-supervised Learning (PSSL) method based on the morphological characteristics of MSP, enabling the model to achieve continuous performance improvement from a large amount of unlabeled data. On the other hand, we incorporate a correction mechanism into the model using neighborhood information in three-dimensional space, providing the model with a certain degree of fault tolerance. Extensive validation conducted on five datasets (comprising millions of brain sections) indicates that our method outperforms state-of-the-art approaches in midline detection within the brain.

JBHI Journal 2025 Journal Article

Exploring the Potential of SSVER-BCI Based on Contactless Measurement Using Optically Pumped Magnetometers

• Fulong Wang
• Fuzhi Cao
• Jiawei Gao
• Nan An
• Jianzhi Yang
• Yaxiang Wang
• Dexin Yu
• Xin Ma

Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have been widely applied in health monitoring and neurorehabilitation. However, EEG signals are often attenuated and distorted by tissues like the scalp and skull, limiting EEG-based BCI performance. In contrast, magnetoencephalography (MEG) with contactless measurement offers higher spatial resolution and immunity to volume conduction effects. Traditional MEG systems, based on superconducting quantum interference devices (SQUIDs), are hindered by their size and cost, while optically pumped magnetometers (OPMs) have made OPM-MEG-based BCIs more practical and accessible. Nevertheless, the performance potential of OPM-MEG in BCI applications remains underexplored. To address this, we developed an OPM-MEG BCI system based on steady-state visual evoked response (SSVER) and conducted a systematic evaluation of its performance, highlighting the practical advantages of OPM-MEG in this context. Furthermore, we proposed a fusion framework for OPM-MEG and EEG to further enhance system performance. Offline experiments conducted with 13 participants showed that the developed EEG-BCI achieved an average accuracy of 94. 30% and an information transfer rate (ITR) of 122. 76 bits/min, the developed OPM-MEG BCI achieved an average accuracy of 98. 68% and an ITR of 138. 20 bits/min, while the hybrid BCI achieved an average accuracy of 99. 72% and an ITR of 159. 4 bits/min. The findings highlight the advantages of OPM-MEG for BCI applications and validate the proposed fusion framework as a viable means to enhance decoding performance, thereby extending the potential use cases of OPM-MEG-based systems.

YNIMG Journal 2025 Journal Article

Extended homogeneous field correction method based on oblique projection in OPM-MEG

• Fulong Wang
• Fuzhi Cao
• Yujie Ma
• Ruochen Zhao
• Ruonan Wang
• Nan An
• Min Xiang
• Dawei Wang

Optically pumped magnetometer-based magnetoencephalography (OPM-MEG) is an novel non-invasive functional imaging technique that features more flexible sensor configurations and wearability; however, this also increases the requirement for environmental noise suppression. Subspace projection algorithms are widely used in MEG to suppress noise. However, in OPM-MEG systems with a limited number of channels, subspace projection methods that rely on spatial oversampling exhibit reduced performance. The homogeneous field correction (HFC) method resolves this problem by constructing a low-rank spatial model; however, it cannot address complex non-homogeneous noise. The spatiotemporal extended homogeneous field correction (teHFC) method uses multiple orthogonal projections to suppress disturbances. However, the signal and noise subspace are not completely orthogonal, limiting enhancement in the capabilities of the teHFC. Therefore, we propose an extended homogeneous field correction method based on oblique projection (opHFC), which overcomes the issue of non-orthogonality between the signal and noise subspace, enhancing the ability to suppress complex interferences. The opHFC constructs an oblique projection operator that divides the signals into internal and external components, eliminating complex interferences through temporal extension. We compared the opHFC with four benchmark methods by simulations and auditory and somatosensory evoked OPM-MEG experiments. The results demonstrate that opHFC provides superior noise suppression with minimal distortion, enhancing the signal quality at the sensor and source levels. Our method offers a novel approach to reducing interference in OPM-MEG systems, expanding their application scenarios, and providing high-quality signals for scientific research and clinical applications based on OPM-MEG.

YNIMG Journal 2025 Journal Article

Investigating the effects of calibration errors on the spatial resolution of OPM-MEG beamformer imaging

• Shengjie Qi
• Xinda Song
• Le Jia
• Zhaoxin Duan
• Yan Dai
• Jing Zhang
• Xiaolin Ning

The use of optically pumped magnetometers (OPMs) has provided a feasible, moveable and wearable alternative to superconducting detectors for magnetoencephalography (MEG) measurements. Recently, the widely used beamformer imaging technique has greatly improved spatial accuracy of MEG in the field of source reconstruction of neuroimaging. The spatial resolution of the source reconstruction using beamformer imaging technique was explored in the present study. The spatial accuracy of a beamformer reconstruction depends on accurate estimation of the data covariance matrix and lead field. In practical measurements, many sensor calibration errors including the gain error, crosstalk and angular error of the sensitive axis of OPMs due to for example, the low frequency magnetic field drift will distort the measured data as well as the forward model and thus reduce spatial resolution. The theory of OPM calibration errors was first provided based on the Bloch equations. The calibration errors are then quantified using the self-developed OPM array. And an analytical relationship between the Frobenius norm of the covariance matrix error and gain error, crosstalk was derived. The relationship between point-spread function (PSF) and the forward model error caused by the angular error of sensitive axis was analyzed. Finally, the effects of calibration errors on spatial resolution of OPM-MEG were investigated using simulations of two dipoles with orthogonal signals at the source level based on realistic head models. We find the presence of calibration errors will decrease the spatial resolution of beamformer reconstruction. And this decrease will become more severe as the signal-to-noise ratio increases.

YNIMG Journal 2025 Journal Article

Noise and artifact suppression in SQUID and wearable OPM-MEG: A systematic review of background, physiological, and Technical interference

• Ruonan Wang
• Yujie Ma
• Ruochen Zhao
• Jin Ding
• Ling Li
• Yanfei Yang
• Fulong Wang
• Zhiqiang Cao

Magnetoencephalography (MEG) is a non-invasive imaging technique that captures neural activity with high spatio-temporal resolution. In recent years, novel wearable devices based on Optically Pumped Magnetometer (OPM) have emerged as a new driving force for advancing MEG due to their cost-effectiveness, portability, and mobility. In practical applications, MEG signals are frequently influenced by various interference sources, resulting in degradation of signal quality. Consequently, numerous suppression techniques have been proposed to overcome these challenges. This manuscript presents a comprehensive review of the most advanced methods for suppressing MEG noise or artifacts, with a specific focus on mitigating background noise, physiological artifacts (such as those caused by heartbeat, eye movements, and muscle contractions), as well as technical artifacts (including system-related artifacts associated with devices, motion-induced artifacts, and metal-induced artifacts). Additionally, the current limitations and challenges of these approaches in real-world scenarios are highlighted. Reviewing nearly a decade of research, there is an urgent need for a lightweight noise analysis framework in the complex measurement environment of wearable OPM-MEG devices. This framework should be capable of effectively detecting, classifying, and suppressing individual and combined MEG interference. By addressing this need, we can enhance the reliability and practicality of MEG signals while advancing brain science research.

YNIMG Journal 2025 Journal Article

Repairbads: An automatic and adaptive method to repair bad channels and segments for OPM-MEG

• Fulong Wang
• Yujie Ma
• Tianyu Gao
• Yue Tao
• Ruonan Wang
• Ruochen Zhao
• Fuzhi Cao
• Yang Gao

The optically pumped magnetometer (OPM) based magnetoencephalography (MEG) system offers advantages such as flexible layout and wearability. However, the position instability or jitter of OPM sensors can result in bad channels and segments, which significantly impede subsequent preprocessing and analysis. Most common methods directly reject or interpolate to repair these bad channels and segments. Direct rejection leads to data loss, and when the number of sensors is limited, interpolation using neighboring sensors can cause significant signal distortion and cannot repair bad segments present in all channels. Therefore, most existing methods are unsuitable for OPM-MEG systems with fewer channels. We introduce an automatic bad segments and bad channels repair method for OPM-MEG, called Repairbads. This method aims to repair all bad data and reduce signal distortion, especially capable of automatically repairing bad segments present in all channels simultaneously. Repairbads employs Riemannian Potato combined with joint decorrelation to project out artifact components, achieving automatic bad segment repair. Then, an adaptive algorithm is used to segment the signal into relatively stable noise data chunks, and the source-estimate-utilizing noise-discarding algorithm is applied to each chunk to achieve automatic bad channel repair. We compared the performance of Repairbads with the Autoreject method on both simulated and real auditory evoked data, using five evaluation metrics for quantitative assessment. The results demonstrate that Repairbads consistently outperforms across all five metrics. In both simulated and real OPM-MEG data, Repairbads shows better performance than current state-of-the-art methods, reliably repairing bad data with minimal distortion. The automation of this method significantly reduces the burden of manual inspection, promoting the automated processing and clinical application of OPM-MEG.

JBHI Journal 2025 Journal Article

SkipDAEformer: A High-Precision Representation Learning Method for Removing Random Mixed Noise in MCG Signals

• Ruizhe Wang
• Zhanyi Liu
• Jiaojiao Pang
• Jie Sun
• Min Xiang
• Xiaolin Ning

Automated analytical techniques for magnetocardiography (MCG) are essential for diagnosing and predicting cardiovascular diseases. Clinically acquired MCG signals are often contaminated by various types of noise, which negatively impact subsequent signal analysis. However, traditional methods have limitations in denoising long-term MCG signals with complex spatial structures. We propose a high-precision, robust representation learning method based on skip connection multi-scale feature fusion (SkipDAEformer) for effectively removing random mixed noise in MCG signals. SkipDAEformer integrates attention fusion mechanisms into a basic denoising autoencoder to extract and fuse critical temporal and spatial information from each feature map, thus enhancing the model’s ability to capture long-range dependencies and spatial features in MCG signals. Meanwhile, we further supplement and refine the semantic information for the feature maps through a global feature fusion method. By fusing multi-scale features from different skip connections, SkipDAEformer can learn more comprehensive representations of MCG signals, enabling the effective separation of clean signals from noise. Experimental results demonstrate that SkipDAEformer outperforms existing methods in denoising performance, channel consistency, feature consistency, and generalization ability and can be extended to a self-supervised learning framework. In actual noise reduction and diagnostic classification tasks, SkipDAEformer shows superior clinical acceptability and diagnostic value, potentially advancing MCG data analysis.

YNIMG Journal 2025 Journal Article

The impact of channel density, inverse solutions, connectivity metrics and calibration errors on OPM-MEG connectivity analysis: A simulation study

• Shengjie Qi
• Xinda Song
• Le Jia
• Hongyu Cui
• Yuchen Suo
• Tengyue Long
• Zhendong Wu
• Xiaolin Ning

Magnetoencephalography (MEG) systems based on optically pumped magnetometers (OPMs) have rapidly developed in the fields of brain function, health, and disease. Functional connectivity analysis related to the resting-state has gained popularity as a field of research in recent years. Several studies have attempted to use OPM-based MEG (OPM-MEG) for brain network estimation research; however, the choice of source connectivity analysis pipeline may lead to outcome variability. Several methods and related parameters must be selected carefully at each step of the analysis. Therefore, this study assessed the effect of such analytical variability on the OPM-MEG connectivity analysis by conducting simulations. Synthetic MEG data corresponding to two default mode networks (DMN) with six or ten DMN regions were generated using the Gaussian Graphical Spectral (GGS) model. Six intersensor spacings were constructed, and six inverse algorithms and six functional connectivity measures were selected to assess their impact on the network reconstruction accuracy. Three potential sources of error - errors in the sensor gain, crosstalk, and angular errors of the sensitive axis of the OPM - were also assessed. Analytical variability with regard to the tested intersensor spacings, inverse solutions, and functional connectivity measures led to high result variability. Crosstalk exerted a significant impact on the accuracy, which may lead to network reconstruction failure. The accuracy improvement caused by an increase in the sensor density may be reduced by gain and angular errors. The minimum norm estimate (MNE) and weighted minimum norm estimate (wMNE) exhibited low robustness to sensor noise and calibration errors. Hence, a calibration workflow for accurate sensor parameters, such as the gain and direction of the sensitive axis, before commencing OPM-MEG measurement and a careful choice of different method combinations play crucial roles in ensuring that OPMs yield optimal results for functional connectivity analysis. A thorough framework for analyzing brain connectivity networks was provided herein.

YNIMG Journal 2024 Journal Article

Expanding the clinical application of OPM-MEG using an effective automatic suppression method for the dental brace metal artifact

• Ruonan Wang
• Kaiwen Fu
• Ruochen Zhao
• Dawei Wang
• Zhimin Yang
• Wei Bin
• Yang Gao
• Xiaolin Ning

Optically pumped magnetometer magnetoencephalography (OPM-MEG) holds significant promise for clinical functional brain imaging due to its superior spatiotemporal resolution. However, effectively suppressing metallic artifacts, particularly from devices such as orthodontic braces and vagal nerve stimulators remains a major challenge, hindering the wider clinical application of wearable OPM-MEG devices. A comprehensive analysis of metal artifact characteristics from time, frequency, and time-frequency perspectives was conducted for the first time using an OPM-MEG device in clinical medicine. This study focused on patients with metal orthodontics, examining the modulation of metal artifacts by breath and head movement, the incomplete regular sub-Gaussian distribution, and the high absolute power ratio in the 0.5-8 Hz band. The existing metal artifact suppression algorithms applied to SQUID-MEG, such as fast independent component analysis (FastICA), information maximization (Infomax), and algorithms for multiple unknown signal extraction (AMUSE), exhibit limited efficacy. Consequently, this study introduced the second-order blind identification (SOBI) algorithm, which utilized multiple time delays for the component separation of OPM-MEG measurement signals. We modified the time delays of the SOBI method to improve its efficacy in separating artifact components, particularly those in the ultralow frequency range. This approach employs the frequency-domain absolute power ratio, root mean square (RMS) value, and mutual information methods to automate the artifact component screening process. The effectiveness of this method was validated through simulation experiments involving four subjects in both resting and evoked experiments. In addition, the proposed method was also validated by the actual OPM-MEG evoked experiments of three subjects. Comparative analyses were conducted against the FastICA, Infomax, and AMUSE algorithms. Evaluation metrics included normalized mean square error, normalized delta band power error, RMS error, and signal-to-noise ratio, demonstrating that the proposed method provides optimal suppression of metal artifacts. This advancement holds promise for enhancing data quality and expanding the clinical applications of OPM-MEG.

YNIMG Journal 2024 Journal Article

Source imaging method based on diagonal covariance bases and its applications to OPM-MEG

• Wen Li
• Fuzhi Cao
• Nan An
• Wenli Wang
• Chunhui Wang
• Weinan Xu
• Dexin Yu
• Min Xiang

Magnetoencephalography (MEG) is a noninvasive imaging technique used in neuroscience and clinical research. The source estimation of MEG involves solving a highly underdetermined inverse problem, which requires additional constraints to restrict the solution space. Traditional methods tend to obscure the extent of the sources. However, an accurate estimation of the source extent is important for studying brain activity or preoperatively estimating pathogenic regions. To improve the estimation accuracy of the extended source extent, the spatial constraint of sources is employed in the Bayesian framework. For example, the source is decomposed into a linear combination of validated spatial basis functions, which is proved to improve the source imaging accuracy. In this work, we further construct the spatial properties of the source using the diagonal covariance bases (DCB), which we summarize as the source imaging method SI-DCB. In this approach, specifically, the covariance matrix of the spatial coefficients is modeled as a weighted combination of diagonal covariance basis functions. The convex analysis is used to estimate noise and model parameters under the Bayesian framework. Extensive numerical simulations showed that SI-DCB outperformed five benchmark methods in accurately estimating the location and extent of patch sources. The effectiveness of SI-DCB was verified through somatosensory stimulation experiments performed on a 31-channel OPM-MEG system. The SI-DCB correctly identified the source area where each brain response occurred. The superior performance of SI-DCB suggests that it can provide a template approach for improving the accuracy of source extent estimations under a sparse Bayesian framework.