Biology subjects

Jas, M.

Publications and source records attributed to Jas, M..

12 recordsLinked to original sources

bioRxiv2026

Signal-to-noise ratio of event-related fields in on-scalp and off-scalp MEG

Abstract Optically pumped magnetometer (OPM) sensors can be placed closer to the scalp than conventional superconducting quantum interference devices (SQUID), resulting in larger magnetoencephalography (MEG) signals from neuronal activity. For event-related sensor data, such as epileptogenic activity or sensory and motor evoked responses, however, OPMs and SQUIDs often differ less in signal-to-noise ratio (SNR) than in signal magnitude. We examined two factors contributing to the relative SNR: the dependence of the signal magnitude on source depth and the effect of scalp-to-sensor distance on the noise level. Simulated MEG data for a current dipole in a spherical head model confirmed that on-scalp sensor placement delivers the largest SNR gain for superficial sources. Depending on the relative overall noise level, there may be a crossover source depth at which SNR is equal for on-scalp and off-scalp sensors and beyond which off-scalp sensors achieve higher SNR. Analysis of the equal-SNR source depth in different-sized spherical head models indicated that, for a given relative noise level, the proportion of the brain where SNR is higher in OPM than in SQUID was larger in small head models, supporting the benefits of OPMs in pediatric studies. To experimentally evaluate noise contributions of brain and non-brain origin to the SNR, we recorded somatosensory evoked fields (SEFs) at varying scalp-to-sensor distances. Generally, both the evoked response magnitude and the noise level were lower when the sensors were further away from the scalp; consequently, the SNR depended less than the signal magnitude on the scalp-to-sensor distance. Comparison of power spectral densities (PSDs) at different sensor-to-scalp distances allowed us to identify whether the dominant noise source was of brain or non-brain origin at different frequency bands. Overall, the results highlight complementary properties of OPMs vs. SQUIDs in terms of SNR, which is of interest when optimizing MEG experiments for specific subject populations and brain regions.

neuroscience↗

bioRxiv2026

Motion tolerance in wearable OPM-MEG using dynamic field nulling

Wearable magnetoencephalography (MEG) enabled by optically pumped magnetometers (OPMs) promises improved comfort and motion tolerance. This is particularly beneficial when measuring brain activity in children who cannot sit still for long periods of time. Compared to cryogenic MEG, wearable MEG allows larger head movements, but they result in artifacts due to uncompensated background fields and reduce source localization accuracy. Spatial filtering methods can partially compensate these motion-induced artifacts, but they are most effective when used in combination with background field nulling. This is because accurate spatial filtering relies on an accurate estimate of the sensor gain and orientation of its sensitive axis. Through simulations, we first deduce the target residual background field that is necessary for accurate dipole localization (< 1 cm) in the presence of head movements. Using our open-source printed circuit board (PCB) coils, we develop a method to dynamically null the background field. We demonstrate that our dynamic field nulling method allows improved localization of somatosensory evoked fields (SEFs) by maintaining the background field below the target residual fields established in the simulations. Our study highlights the importance of tracking both the background field and the head position relative to the background field for quality assurance in wearable MEG.

neuroscience↗

bioRxiv2026

Quantifying Cerebellar Signal Detectability in MEG and EEG in Epilepsy Using Anatomically Informed Source Modeling

ObjectiveThe cerebellum is increasingly recognized as a key component of large-scale brain networks implicated in epilepsy, yet its electrophysiological characterization remains limited in noninvasive recordings. This limitation arises from the cerebellums depth, complex folding, and unfavorable source orientations, which challenge conventional magnetoencephalography (MEG) and electroencephalography (EEG). Here, we quantitatively characterize cerebellar signal detectability across modalities and sensor configurations using anatomically informed source modeling at the population level. MethodsWe analyzed clinical MEG and EEG recordings from a large cohort of patients with epilepsy undergoing presurgical evaluation. Cerebellar and cerebral source spaces were constructed using subject-specific anatomical models derived from routine clinical MRI, enabling consistent forward modeling across individuals. Signal-to-noise ratio (SNR) was estimated at individual source locations and summarized at the regional level. In addition to clinical Superconducting quantum interference device (SQUID)-MEG and EEG, multiple on-scalp optically pumped magnetometer (OPM) configurations were evaluated through simulation, including layouts matched to clinical sensor geometries and layouts optimized for posterior fossa coverage. The effects of source orientation, sensor-source distance, and head size on SNR were systematically investigated. ResultsIn routine clinical recordings, cerebellar SNR was consistently lower than superficial cortical reference levels, confirming the limited detectability of cerebellar activity with standard SQUID-MEG and EEG. Reducing sensor-source distance by placing OPMs at SQUID-equivalent locations, i.e., projecting SQUID sensor locations to the scalp, did not improve cerebellar SNR, indicating that proximity alone is insufficient for better detectability of deeper sources. In contrast, cerebellar-optimized OPM layouts produced substantial SNR gains in posterior cerebellar regions. The effects of source orientation influence SNR differences between OPM and EEG (under identical sensor/electrode coverage) but were secondary to depth- and geometry-related constraints. Mediation analysis further demonstrated that relative sensor distance significantly mediated OPM-related advantages in posterior cerebellar regions, particularly in individuals with smaller head sizes. ConclusionsThese findings demonstrate that cerebellar signal detectability is governed primarily by anatomical depth and geometry rather than sensor proximity alone. Anatomically informed source modeling, combined with flexible and region-specific sensor layouts, enables meaningful improvements in cerebellar SNR that are not achievable with fixed-helmet systems. While directly motivated by epilepsy, this framework advances human brain mapping beyond the cerebrum by providing a principled approach for evaluating MEG and EEG sensitivity in deep and highly folded brain structures.

bioengineering↗

bioRxiv2025

Biplanar nulling coil system for OPM-MEG using printed circuit boards

Optically pumped magnetometers (OPMs) are a promising sensor technology for non-invasive measurement of human electrophysiological signals, in particular the magnetoencephalogram (MEG). OPMs do not need cryogenic cooling and can be placed conformal to the subjects scalp, thus greatly reducing the sensor-to-source distance and improving signal sensitivity. OPMs, however, require near-zero background magnetic field to achieve linearity and minimize signal distortion. Prior work has proposed the use of biplanar field nulling coils to remove the uniform and gradient components of the background magnetic field. Biplanar coils have been expensive to construct, involving tedious error-prone manual winding of over 1000 m of copper wire. In this work, we designed and fabricated background field nulling coils (three uniform and three gradient components) on two-layer Printed Circuit Boards (PCBs). We used an open-source software (bfieldtools) to determine the current loops needed to produce the target magnetic field in a 50-cm-diameter spherical volume. We developed a software-based approach to connect the discrete current loops into a continuous conducting path traversing the two layers of the PCB. For ease of manufacture, the designed (1.5 x 1.5 m2) coils were cut along the symmetry axis and printed as pairs of 1.5 x 0.75 m2 PCBs (2 oz Cu), soldered together and mounted on a sliding aluminum frame. The efficiency of the coils (1.3 - 7.1 nT/mA) was similar or higher than previously reported in the literature. We mapped the field inside the target region after field nulling inside our single-layer shielded room and were able to reduce the largest component of the background field from 21 to 2 nT. Using our nulling coil system, we were able to operate OPMs in a lightly shielded room (background field varying from 6.5 to 108 nT in the floor-to-ceiling direction) to record somatosensory evoked fields (SEFs) comparable to those measured using SQUID-based MEG in a 3-layer shielded room. We disseminate the software and hardware as an open-source package opmcoils. This work will facilitate access to more affordable field nulling coils for OPM-MEG and help to realize the potential of OPM-MEG as an accessible sensor technology for use in human neuroscience.

neuroscience↗

bioRxiv2024

Decoding auditory working memory content from EEG aftereffects of auditory-cortical TMS

Working memory (WM), short term maintenance of information for goal directed behavior, is essential to human cognition. Identifying the neural mechanisms supporting WM is a focal point of neuroscientific research. One prominent theory hypothesizes that WM content is carried in "activity-silent" brain states involving short-term synaptic changes. Information carried in such brain states could be decodable from content-specific changes in responses to unrelated "impulse stimuli". Here, we used single-pulse transcranial magnetic stimulation (spTMS) as the impulse stimulus and then decoded content maintained in WM from EEG using multivariate pattern analysis (MVPA) with robust non-parametric permutation testing. The decoding accuracy of WM content significantly enhanced after spTMS was delivered to the posterior superior temporal cortex during WM maintenance. Our results show that WM maintenance involves brain states, which are activity silent relative to other intrinsic processes visible in the EEG signal.

neuroscience↗

bioRxiv2024

Characterizing directional dynamics of semantic prediction based on inter-regional temporal generalization

The event-related potential/field component N400(m) has been widely used as a neural index for semantic prediction. It has long been hypothesized that feedback information from inferior frontal areas plays a critical role in generating the N400. However, due to limitations in causal connectivity estimation, direct testing of this hypothesis has remained difficult. Here, magnetoencephalography (MEG) data was obtained during a classic N400 paradigm where the semantic predictability of a fixed target noun was manipulated in simple German sentences. To estimate causality, we implemented a novel approach based on machine learning and temporal generalization to estimate the effect of inferior frontal gyrus (IFG) on temporal areas. In this method, a support vector machine (SVM) classifier is trained on each time point of the neural activity in IFG to classify less predicted (LP) and highly predicted (HP) nouns and then tested on all time points of superior/middle temporal sub- regions activity (and vice versa, to establish spatio-temporal evidence for or against causality). The decoding accuracy was significantly above chance level when the classifier was trained on IFG activity and tested on future activity in superior and middle temporal gyrus (STG/MTG). The results present new evidence for a model predictive speech comprehension where predictive IFG activity is fed back to shape subsequent activity in STG/MTG, implying a feedback mechanism in N400 generation. In combination with the also observed strong feedforward effect from left STG/MTG to IFG, our findings provide evidence of dynamic feedback and feedforward influences between IFG and temporal areas during N400 generation.

neuroscience↗

bioRxiv2023

Neuronal modeling of magnetoencephalography responses in auditory cortex to auditory and visual stimuli

Previous studies have demonstrated that auditory cortex activity can be influenced by crosssensory visual inputs. Intracortical recordings in non-human primates (NHP) have suggested a bottom-up feedforward (FF) type laminar profile for auditory evoked but top-down feedback (FB) type for cross-sensory visual evoked activity in the auditory cortex. To test whether this principle applies also to humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) evoked by simple auditory or visual stimuli. In the estimated MEG source waveforms for auditory cortex region of interest, auditory evoked responses showed peaks at 37 and 90 ms and cross-sensory visual responses at 125 ms. The inputs to the auditory cortex were then modeled through FF and FB type connections targeting different cortical layers using the Human Neocortical Neurosolver (HNN), which consists of a neocortical circuit model linking the cellular- and circuit-level mechanisms to MEG. The HNN models suggested that the measured auditory response could be explained by an FF input followed by an FB input, and the crosssensory visual response by an FB input. Thus, the combined MEG and HNN results support the hypothesis that cross-sensory visual input in the auditory cortex is of FB type. The results also illustrate how the dynamic patterns of the estimated MEG/EEG source activity can provide information about the characteristics of the input into a cortical area in terms of the hierarchical organization among areas. Significance statementLaminar intracortical profiles of activity characterize feedforward- and feedback-type influences in the inputs to a cortical area. By combining magnetoencephalography (MEG) and biophysical computational neural modeling, we obtained evidence of cross-sensory visual evoked activity in human auditory cortex being of feedback type. The finding is consistent with previous intracortical recordings in non-human primates. The results illustrate how patterns of MEG source activity can be interpreted in the context of the hierarchical organization among cortical areas.

neuroscience↗

bioRxiv2019

NeuroPycon: A free Python toolbox for fast multi-modal and reproducible brain connectivity pipelines

Recent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses. Importantly, it provides shareable parameter files to facilitate replication of all analysis steps. NeuroPycon is based on the NiPype framework which facilitates data analyses by wrapping many commonly-used neuroimaging software tools into a common Python environment. In other words, rather than being a brain imaging software with is own implementation of standard algorithms for brain signal processing, NeuroPycon seamlessly integrates existing packages (coded in python, Matlab or other languages) into a unified python framework. Importantly, thanks to the multi-threaded processing and computational efficiency afforded by NiPype, NeuroPycon provides an easy option for fast parallel processing, which critical when handling large sets of multi-dimensional brain data. Moreover, its flexible design allows users to easily configure analysis pipelines by connecting distinct nodes to each other. Each node can be a Python-wrapped module, a user-defined function or a well-established tool (e.g. MNE-Python for MEG analysis, Radatools for graph theoretical metrics, etc.). Last but not least, the ability to use NeuroPycon parameter files to fully describe any pipeline is an important feature for reproducibility, as they can be shared and used for easy replication by others. The current implementation of NeuroPycon contains two complementary packages: The first, called ephypype, includes pipelines for electrophysiology analysis and a command-line interface for on the fly pipeline creation. Current implementations allow for MEG/EEG data import, pre-processing and cleaning by automatic removal of ocular and cardiac artefacts, in addition to sensor or source-level connectivity analyses. The second package, called graphpype, is designed to investigate functional connectivity via a wide range of graph-theoretical metrics, including modular partitions. The present article describes the philosophy, architecture, and functionalities of the toolkit and provides illustrative examples through interactive notebooks. NeuroPycon is available for download via github (https://github.com/neuropycon) and the two principal packages are documented online (https://neuropycon.github.io/ephypype/index.html. and https://neuropycon.github.io/graphpype/index.html). Future developments include fusion of multi-modal data (eg. MEG and fMRI or intracranial EEG and fMRI). We hope that the release of NeuroPycon will attract many users and new contributors, and facilitate the efforts of our community towards open source tool sharing and development, as well as scientific reproducibility.

neuroscience↗

bioRxiv2019

Target of selective auditory attention can be robustly followed with MEG

Selective auditory attention enables filtering of relevant acoustic information from irrelevant. Specific auditory responses, measurable by magneto- and electroencephalography (MEG/EEG), are known to be modulated by attention to the evoking stimuli. However, such attention effects have typically been studied in unnatural conditions (e.g. during dichotic listening of pure tones) and have been demonstrated mostly in averaged auditory evoked responses. To test how reliably we can detect the attention target from unaveraged brain responses, we recorded MEG data from 15 healthy subjects that were presented with two human speakers uttering continuously the words "Yes" and "No" in an interleaved manner. The subjects were asked to attend to one speaker. To investigate which temporal and spatial aspects of the responses carry the most information about the target of auditory attention, we performed spatially and temporally resolved classification of the unaveraged MEG responses using a support vector machine. Sensor-level decoding of the responses to attended vs. unattended words resulted in a mean accuracy of 79% {+/-} 2% (N = 14) for both stimulus words. The discriminating information was mostly available 200-400 ms after the stimulus onset. Spatially-resolved source-level decoding indicated that the most informative sources were in the auditory cortices, in both the left and right hemisphere. Our result corroborates attention modulation of auditory evoked responses and shows that such modulations are detectable in unaveraged MEG responses at high accuracy, which could be exploited e.g. in an intuitive brain-computer interface.

neuroscience↗

bioRxiv2019

Mammogram Segmentation using Multi-atlas Deformable Registration

Accurate breast region segmentation is an important step in various automated algorithms involving detection of lesions like masses and microcalcifications, and efficient telemammography. Existing segmentation algorithms underperform due to variations in image quality and shape of the breast region. In this paper, we propose to segment breast region by combining data-driven clustering with deformable image registration. In the first phase of the approach, we identify atlas images from a dataset of mammograms using data-driven clustering. Then, we segment these atlas images and use in the next phase of the algorithm. The second phase is atlas-based registration. For a candidate image, we find the most similar atlas image from the set of atlases identified in phase one. We deform the selected atlas image to match the given test image using the Demon's registration algorithm. Then, the segmentation mask of the deformed atlas is transferred to the mammogram in consideration. Finally, we refine the segmentation mask with some morphological operations in order to obtain accurate breast region boundary. We evaluated the performance of our method using ground-truth segmentation masks verified by an expert radiologist. We compared the proposed method with three existing state-of-the-art algorithms for breast region segmentation and the proposed approach outperformed all three in most of the cases.

bioinformatics↗

bioRxiv2017

MEG/EEG group study with MNE: recommendations, quality assessments and best practices

Cognitive neuroscience questions are commonly tested with experiments that involve a cohort of subjects. The cohort can consist of a handful of subjects for small studies to hundreds or thousands of subjects in open datasets.\n\nWhile there exist various online resources to get started with the analysis of magnetoencephalography (MEG) or electroencephalography (EEG) data, such educational materials are usually restricted to the analysis of a single subject. This is in part because data from larger group studies are harder to share, but also analyses of such data are often require subject-specific decisions which are hard to document.\n\nThis work presents the results obtained by the reanalysis of an open dataset from Wakeman and Henson (2015) using the MNE software package. The analysis covers preprocessing steps, quality assurance steps, sensor space analysis of evoked responses, source localization, and statistics in both sensor and source space. Results with possible alternative strategies are presented and discussed at different stages such as the use of high-pass filtering versus baseline correction, tSSS versus SSS, the use of a minimum norm inverse versus LCMV beamformer, and the use of univariate or multivariate statistics. This aims to provide a comparative study of different stages of M/EEG analysis pipeline on the same dataset, with open access to all of the scripts necessary to reproduce this analysis.

neuroscience↗

bioRxiv2017

MEG-BIDS: an extension to the Brain Imaging Data Structure for magnetoencephalography

We present a significant extension of the Brain Imaging Data Structure (BIDS) to support the specific aspects of magnetoencephalography (MEG) data. MEG provides direct measurement of brain activity with millisecond temporal resolution and unique source imaging capabilities. So far, BIDS has provided a solution to structure the organization of magnetic resonance imaging (MRI) data, which nature and acquisition parameters are different. Despite the lack of standard data format for MEG, MEG-BIDS is a principled solution to store, organize and share the typically-large data volumes produced. It builds on BIDS for MRI, and therefore readily yields a multimodal data organization by construction. This is particularly valuable for the anatomical and functional registration of MEG source imaging with MRI. With MEG-BIDS and a growing range of software adopting the standard, the MEG community has a solution to minimize curation overheads, reduce data handling errors and optimize usage of computational resources for analytics. The standard also includes well-defined metadata, to facilitate future data harmonization and sharing efforts.

neuroscience↗