Author name cluster

Mainak Jas

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

YNIMG Journal 2026 Journal Article

Quantifying cerebellar signal detectability in MEG and EEG in epilepsy using anatomically informed source modeling

Teppei Matsubara
Abbas Sohrabpour
Seppo P. Ahlfors
Mainak Jas
John Samuelsson
Padmavathi Sundaram
Steven Stufflebeam

The cerebellum is increasingly recognized as a key component of large-scale brain networks implicated in epilepsy, yet its electrophysiological characterization remains limited in non-invasive recordings. This limitation arises from the cerebellum's 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. We analyzed clinical MEG and EEG recordings from a large cohort of patients with epilepsy undergoing presurgical evaluation (n = 54), selected from a larger consecutive clinical population. Cerebellar and cerebral source spaces were constructed using subject-specific anatomical models derived from routine clinical MRI, enabling consistent forward modeling across individuals. The 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 simulations, including layouts matched to clinical sensor geometries and layouts optimized for posterior fossa coverage. The effects of source orientation, source-to-sensor distance, and head size on SNR were systematically investigated. In routine clinical recordings, cerebellar SNR was consistently lower than superficial cortical reference levels, confirming challenges in detecting cerebellar activity using standard SQUID-MEG and EEG. Reducing source-to-sensor 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 influenced 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. These findings demonstrate that cerebellar signal detectability is governed primarily by anatomical depth and geometry rather than sensor proximity alone. By combining anatomically informed source modeling with flexible, region-specific sensor layouts, this work provides a principled framework for evaluating and improving MEG and EEG sensitivity to cerebellar activity, with implications extending beyond epilepsy to non-invasive mapping of deep and highly folded brain structures.

Details DOI

YNIMG Journal 2020 Journal Article

NeuroPycon: An open-source python toolbox for fast multi-modal and reproducible brain connectivity pipelines

David Meunier
Annalisa Pascarella
Dmitrii Altukhov
Mainak Jas
Etienne Combrisson
Tarek Lajnef
Daphné Bertrand-Dubois
Vanessa Hadid

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.

Details DOI

NeurIPS Conference 2018 Conference Paper

Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals

Tom Dupré la Tour
Thomas Moreau
Mainak Jas
Alexandre Gramfort

Frequency-specific patterns of neural activity are traditionally interpreted as sustained rhythmic oscillations, and related to cognitive mechanisms such as attention, high level visual processing or motor control. While alpha waves (8--12\, Hz) are known to closely resemble short sinusoids, and thus are revealed by Fourier analysis or wavelet transforms, there is an evolving debate that electromagnetic neural signals are composed of more complex waveforms that cannot be analyzed by linear filters and traditional signal representations. In this paper, we propose to learn dedicated representations of such recordings using a multivariate convolutional sparse coding (CSC) algorithm. Applied to electroencephalography (EEG) or magnetoencephalography (MEG) data, this method is able to learn not only prototypical temporal waveforms, but also associated spatial patterns so their origin can be localized in the brain. Our algorithm is based on alternated minimization and a greedy coordinate descent solver that leads to state-of-the-art running time on long time series. To demonstrate the implications of this method, we apply it to MEG data and show that it is able to recover biological artifacts. More remarkably, our approach also reveals the presence of non-sinusoidal mu-shaped patterns, along with their topographic maps related to the somatosensory cortex.

PDF Details

YNIMG Journal 2017 Journal Article

Autoreject: Automated artifact rejection for MEG and EEG data

Mainak Jas
Denis A. Engemann
Yousra Bekhti
Federico Raimondo
Alexandre Gramfort

We present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold – a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors. Depending on the number of bad sensors, the trial is then repaired by interpolation or by excluding it from subsequent analysis. All steps of the algorithm are fully automated thus lending itself to the name Autoreject. In order to assess the practical significance of the algorithm, we conducted extensive validation and comparisons with state-of-the-art methods on four public datasets containing MEG and EEG recordings from more than 200 subjects. The comparisons include purely qualitative efforts as well as quantitatively benchmarking against human supervised and semi-automated preprocessing pipelines. The algorithm allowed us to automate the preprocessing of MEG data from the Human Connectome Project (HCP) going up to the computation of the evoked responses. The automated nature of our method minimizes the burden of human inspection, hence supporting scalability and reliability demanded by data analysis in modern neuroscience.

Details DOI

NeurIPS Conference 2017 Conference Paper

Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding

Mainak Jas
Tom Dupré la Tour
Umut Simsekli
Alexandre Gramfort

Neural time-series data contain a wide variety of prototypical signal waveforms (atoms) that are of significant importance in clinical and cognitive research. One of the goals for analyzing such data is hence to extract such `shift-invariant' atoms. Even though some success has been reported with existing algorithms, they are limited in applicability due to their heuristic nature. Moreover, they are often vulnerable to artifacts and impulsive noise, which are typically present in raw neural recordings. In this study, we address these issues and propose a novel probabilistic convolutional sparse coding (CSC) model for learning shift-invariant atoms from raw neural signals containing potentially severe artifacts. In the core of our model, which we call $\alpha$CSC, lies a family of heavy-tailed distributions called $\alpha$-stable distributions. We develop a novel, computationally efficient Monte Carlo expectation-maximization algorithm for inference. The maximization step boils down to a weighted CSC problem, for which we develop a computationally efficient optimization algorithm. Our results show that the proposed algorithm achieves state-of-the-art convergence speeds. Besides, $\alpha$CSC is significantly more robust to artifacts when compared to three competing algorithms: it can extract spike bursts, oscillations, and even reveal more subtle phenomena such as cross-frequency coupling when applied to noisy neural time series.

PDF Details