Deep brain stimulation (DBS) is a critical neuromodulation technique that has been widely applied in the treatment of neurological disorders such as Parkinson's disease (PD) and epilepsy. As an important functional neuroimaging modality, magnetoencephalography (MEG) has played a key role in DBS research. In particular, the next-generation MEG based on optically pumped magnetometers (OPM-MEG), offers greater potential for clinical applications. However, the strong electromagnetic interference generated by DBS systems makes data acquisition and analysis challenging in OPM-MEG recordings from patients with implanted devices. To the best of our knowledge, there have been no studies that systematically investigate the characteristics or suppression of DBS-induced artifacts in OPM-MEG recordings from human subjects. In this paper, we describe a novel OPM-MEG interference suppression algorithm called extended homogeneous field correction based on oblique projection (opHFC), developed for suppressing environmental noise in OPM-MEG. To illustrate the practical application of opHFC in clinical settings, particularly for patients with DBS implants. We evaluate the performance of opHFC in denoising OPM-MEG data from PD patients with DBS implants. By applying opHFC to real-world clinical data, we assess its ability to reduce DBS-induced artifacts while preserving neural activity patterns and conduct a comprehensive comparison between opHFC and several commonly used artifact suppression techniques in OPM-MEG. Our results show that opHFC significantly enhances signal quality and achieves the most effective suppression performance, demonstrating its potential as a reliable tool for advancing OPM-MEG applications in challenging clinical environments. This study highlights the practical value of opHFC in improving OPM-MEG data quality for PD patients with DBS, paving the way for more accurate neuroscientific research and clinical diagnostics.

Magnetocardiography (MCG) is gaining prominence in medical technology. However, owing to the semi-open magnetic shielding, MCG is still severely interfered by low-frequency, non-Gaussian noise, particularly in clinical settings. The spatial distribution of low-frequency non-Gaussian noise is not accurately captured by linear mixing models. In addition, this noise completely overlaps with MCG signals in both the time and frequency domains, distorting the physiological information encoded in the waveform morphology and two-dimensional MCG image, which is important for diagnosis. To address this, we propose a multi-channel non-local means (NLM) method based on Hermite approximation, exploiting the high synchronization between channels and the repeatability within each channel without requiring additional reference channels. First, a matrix that contains magnetocardiographic image morphology information is computed through Hermite approximation of the reference channels. Next, clustering is performed on all data, and the standard deviation of the clustering results is utilized to calculate the adaptive Gaussian smoothing parameters. Finally, the multi-channel adaptive NLM algorithm is applied to denoise the MCG signals. Simulation, semi-physical, and real-case experiments using self-developed MCG equipment demonstrate that the proposed method effectively restores the waveform characteristics and time-frequency domain information of MCG images under low-frequency non-Gaussian noise. This method outperforms existing techniques in noise reduction and establishes a solid foundation for future clinical applications.

The MINI-magnetically shielded cabin (MINI-MSC) offers a near-zero magnetic environment for optically pumped atomic magnetometers (OPMs) to measure the magnetocardiography (MCG) and magnetoencephalography (MEG). However, the MINI-MSC’s confined interior and restricted uniform area make it challenging to capture high-quality bodily signals. Once the external interference surpasses the passive magnetic shielding capacity of the MINI-MSC, the volume and the homogeneity of the uniform region are reduced significantly, leading to the poor quality of the signal collected by OPMs. Therefore, an uniform active magnetic compensation system (UAMCS) composed of bi-planar coils (BCs) is introduced to suppress interference for enlarging the uniform region. The performance of the UAMCS depends on the BCs, but under finite-size ferromagnetic boundaries, approximate magnetic field values that deviate from reality are usually provided by the image method with the inaccurate calculation. To overcome these limitations, an analytical expression of the absorption current is derived to accurately characterize the coupling effect, thereby maintaining the high uniformity performance effectively. A forward analytical model is proposed to characterize magnetic fields, and the equivalent current could be analytically determined, which are put into the target field method (TFM) to express the superimposed the magnetic field. The inhomogeneity errors of BCs using the forward analytical model are 0.39 times of the image method. After the magnetic compensation, the signal-to-noise ratio at 0.1 Hz, 1 Hz and 10 Hz were improved by 39.5 dB, 34.3 dB, and 6.7 dB, respectively, verifying the magnetic noise can be well compensated in the whole target region. Note to Practitioners—The challenge of ensuring high signal quality in MINI-MSC for OPMs is critical in biomedical imaging applications. An forward analytical method is introduced to character the absorbed magnetic field of shielding layers, and an advanced UAMCS utilizing BCs is used for the precise magnetic field compensation to significantly enhance the signal-to-noise ratio. This approach addresses the limitations of traditional image methods, offering improved accuracy in magnetic compensation and enhance the reliability of MEG and MCG tests.

Structural variation (SV) refers to insertions, deletions, inversions, and duplications in human genomes. SVs are present in approximately 1.5% of the human genome. Still, this small subset of genetic variation has been implicated in the pathogenesis of psoriasis, Crohn's disease and other autoimmune disorders, autism spectrum and other neurodevelopmental disorders, and schizophrenia. Since identifying structural variants is an important problem in genetics, several specialized computational techniques have been developed to detect structural variants directly from sequencing data. With advances in whole-genome sequencing (WGS) technologies, a plethora of SV detection methods have been developed. However, dissecting SVs from WGS data remains a challenge, with the majority of SV detection methods prone to a high false-positive rate, and no existing method able to precisely detect a full range of SVs present in a sample. Previous studies have shown that none of the existing SV callers can maintain high accuracy across various SV lengths and genomic coverages. Here, we report an integrated structural variant calling framework, Variant Identification and Structural Variant Analysis (VISTA), that leverages the results of individual callers using a novel and robust filtering and merging algorithm. In contrast to existing consensus-based tools which ignore the length and coverage, VISTA overcomes this limitation by executing various combinations of top-performing callers based on variant length and genomic coverage to generate SV events with high accuracy. We evaluated the performance of VISTA on comprehensive gold-standard datasets across varying organisms and coverage. We benchmarked VISTA using the Genome-in-a-Bottle gold standard SV set, haplotype-resolved de novo assemblies from the Human Pangenome Reference Consortium, along with an in-house polymerase chain reaction (PCR)-validated mouse gold standard set. VISTA maintained the highest F1 score among top consensus-based tools measured using a comprehensive gold standard across both mouse and human genomes. VISTA also has an optimized mode, where the calls can be optimized for precision or recall. VISTA-optimized can attain 100% precision and the highest sensitivity among other variant callers. In conclusion, VISTA represents a significant advancement in structural variant calling, offering a robust and accurate framework that outperforms existing consensus-based tools and sets a new standard for SV detection in genomic research.