Biology subjects

Hecker, L.

Publications and source records attributed to Hecker, L..

8 recordsLinked to original sources

bioRxiv2026

invertmeeg: A Unified Python Library and Benchmark for 112 M/EEG Inverse Solvers

Electroencephalography (EEG) source imaging remains difficult to compare systematically because inverse solvers are distributed across different software packages, programming languages, and evaluation protocols. We present a frozen four-scenario EEG benchmark of 106 solvers evaluated on a shared BioSemi-32 / ico3 setup, together with invertmeeg, an open-source Python package that currently exposes 118 inverse solvers through a consistent two-step interface built on the MNE-Python ecosystem. The benchmark spans focal, multi-source, spatially extended, and low-SNR source configurations and uses earth movers distance (EMD) as the primary metric, with average precision (AP), mean localization error (MLE), and correlation used for complementary ranking. Across this benchmark, no single solver dominates every regime: flexible subspace and hybrid methods perform best overall, while Bayesian methods remain particularly competitive under extended-source and low-SNR conditions. The package is available via pip install invertmeeg and imported in Python as invert.

neuroscience↗

bioRxiv2025

Single cell transcriptomics in a treatment-segregated cohort exposes a STAT3-regulated therapeutic gap in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic pulmonary disease of unknown etiology. Since approved IPF drugs only slow disease progression, novel therapeutics are required that improve clinical outcomes. Here we report a single cell lung RNA-Seq and gene regulatory network analysis of the largest IPF cohort assembled to date. Segregating this cohort based on status of treatment with approved first-generation IPF antifibrotics (untreated, nintedanib- and pirfenidone-treated), we describe for the first time the transcriptional landscape of untreated IPF across 40 lung cell types, and the elements of this program that are impacted by these antifibrotics. On average, nearly 60% of the untreated IPF-dysregulated transcriptome is refractory to treatment with these drugs, a transcriptional deficit we refer to as the IPF therapeutic gap. Gene regulatory network analysis indicated a dominant functional footprint for the transcription factor STAT3 in both untreated IPF and the IPF therapeutic gap. Validating our analysis in a translational precision cut lung slice platform that recapitulates IPF explants, pharmacological inhibition of STAT3 reduced the IPF therapeutic gap in numerous lung cell types. Finally, we resolved a STAT3-anchored master regulatory network comprising numerous profibrotic transcription factors in IPF alveolar fibroblasts, a critical fibrotic lineage. Our study represents a comprehensive resource for translational lung fibrosis research and introduces a strategy for drug discovery that is adaptable to human disease more broadly.

cell biology↗

bioRxiv2025

Localization of Realistic Spatial Patches of Complex Source Activity in MEG

Accurate localization of neural sources in Magnetoencephalography (MEG) and Electroencephalography (EEG) is essential for advancing clinical and research applications in neuroscience. Traditional approaches like dipole fitting (e.g., MUSIC, RAP-MUSIC) are limited to discrete focal sources, while distributed source imaging methods (e.g., MNE, sLORETA) assume sources distributed across the cortical surface. These methods, however, often fail to capture sources with complex spatial extents, limiting their accuracy in realistic settings. To address these limitations, we introduce PATCH-AP, an enhanced version of the Alternating Projection (AP) method that effectively localizes both discrete and spatially extended sources. We evaluated PATCH-AP against leading source localization methods, including distributed source imaging techniques (MNE, sLORETA), traditional dipole fitting (AP), and recent extended source methods (Convexity-Champagne (CC), FLEX-AP). PATCH-AP consistently outperformed these methods in simulations, achieving lower Earth Movers Distance (EMD) scores--a metric indicating closer alignment with the true source distribution. In tests with real MEG data from a face perception task, PATCH-AP demonstrated high alignment with the fusiform face area, a region critical for face processing. These results highlight PATCH-APs potential to enhance source localization accuracy, promising significant advancements in neuroscience research and clinical diagnostics.

neuroscience↗

bioRxiv2023

Localization of Spatially Extended Brain Sources by Flexible Alternating Projections (FLEX-AP)

Magnetoencephalography (MEG) and electroencephalography (EEG) are widely employed techniques for the in-vivo measurement of neural activity with exceptional temporal resolution. Modeling the neural sources underlying these signals is of high interest for both neuroscience research and pathology. The method of Alternating Projection (AP) was recently shown to outperform the well-established recursively applied and projected multiple signal classification (RAP-MUSIC) algorithm. In this work, we further enhanced AP to allow for source extent estimation, a novel approach termed flexible extent AP (FLEX-AP). We found that FLEX-AP achieves significantly lower errors for spatially coherent sources compared to AP, RAP-MUSIC, and the corresponding extension, FLEX-RAP-MUSIC. We also found an advantage for discrete dipoles under forward modeling errors encountered in real-world scenarios. Together, our results indicate that the FLEX-AP method can unify dipole fitting and distributed source imaging into a single algorithm with promising accuracy.

neuroscience↗

bioRxiv2023

Spontaneous Necker-Cube Reversals are not that Spontaneous

During observation of the ambiguous Necker cube, our perception suddenly reverses between two about equally possible 3D interpretations. During passive observation, perceptual reversals seem to be sudden and spontaneous. A number of theoretical approaches postulate destabilization of neural representations as a precondition for spontaneous reversals of ambiguous figures. In the current study, we focused on possible EEG correlates of perceptual destabilization, that may allow to predict an upcoming perceptual reversal. We presented ambiguous Necker cube stimuli in an onset-paradigm and investigated the neural processes underlying endogenous reversals as compared to perceptual stability across two consecutive stimulus presentations. In a separate experimental condition, disambiguated cube variants were alternated randomly, to exogenously induce perceptual reversals. We compared the EEG immediately before and during endogenous Necker cube reversals with corresponding time windows during exogenously induced perceptual reversals of disambiguated cube variants. For the ambiguous Necker cube stimuli, we found the earliest differences in the EEG between reversal trials and stability trials already one second before a reversal occurred, at bilateral parietal electrodes. The traces remained similar until approximately 1100 ms before a perceived reversal, became maximally different at around 890 ms (p=7.59*10-6, Cohens d=1.35) and remained different until shortly before offset of the stimulus preceding the reversal. No such patterns were found in the case of disambiguated cube variants. The identified EEG effects may reflect destabilized states of neural representations, related to destabilized perceptual states preceding a perceptual reversal. They further indicate that spontaneous Necker cube reversals are most probably not as spontaneous as generally thought. Rather, the destabilization may occur over a longer time scale, at least one second before a reversal event.

neuroscience↗

bioRxiv2023

Source Localization Using Recursively Applied and Projected MUSIC with Flexible Extent Estimation

1Magneto- and electroencephalography (M/EEG) are widespread techniques to measure neural activity in-vivo at a high temporal resolution but low spatial resolution. Locating the neural sources underlying the M/EEG poses an inverse problem, which is ill-posed. We developed a new method based on Recursive Application of Multiple Signal Classification (MUSIC). Our proposed method is able to recover not only the locations but, in contrast to other inverse solutions, also the extent of active brain regions flexibly (FLEX-MUSIC). This is achieved by allowing it to search not only for single dipoles but also dipole clusters of increasing extent to find the best fit during each recursion. FLEX-MUSIC achieved the highest accuracy for both single dipole and extended sources compared to all other methods tested. Remarkably, FLEX-MUSIC was capable to accurately estimate the level of sparsity in the source space (r = 0.82), whereas all other approaches tested failed to do so (r [≤] 0.18). The average computation time of FLEX-MUSIC was considerably lower compared to a popular Bayesian approach and comparable to that of another recursive MUSIC approach and eLORETA. FLEX-MUSIC produces only few errors and was capable to reliably estimate the extent of sources. The accuracy and low computation time of FLEX-MUSIC renders it an improved technique to solve M/EEG inverse problems both in neuroscience research and potentially in pre-surgery diagnostic in epilepsy.

neuroscience↗

bioRxiv2022

Long-Short Term Memory Networks for Electric Source Imaging with Distributed Dipole Models

1Magneto- and electroencephalography (M/EEG) are widespread techniques to measure neural activity in-vivo at a high temporal resolution but relatively low spatial resolution. Locating the sources underlying the M/EEG poses an inverse problem, which is itself ill-posed. In recent years, a new class of source imaging methods was developed based on artificial neural networks. We present a long-short term memory (LSTM) network to solve the M/EEG inverse problem. It integrates low computational cost, exploitation of both the coarse spatial but also the excellent temporal information from the EEG, input flexibility and robustness to noise. We compared the LSTM network with classical inverse solutions using both simulation data and real EEG data, recorded in epileptic patients during intracranial stimulation. The LSTM network shows higher accuracy on multiple metrics and for varying numbers of neural sources, compared to classical inverse solutions but also compared to our alternative architecture without integration of temporal information. The performance of the LSTM network regarding its robustness to noise and low localization errors renders it a promising inverse solution to be considered in future source localization studies and for clinical applications.

neuroscience↗

bioRxiv2020

ConvDip: A convolutional neural network for better M/EEG Source Imaging

1The EEG is a well-established non-invasive method in neuroscientific research and clinical diagnostics. It provides a high temporal but low spatial resolution of brain activity. In order to gain insight about the spatial dynamics of the EEG one has to solve the inverse problem, i.e. finding the neural sources that give rise to the recorded EEG activity. The inverse problem is ill-posed, which means that more than one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. Artificial neural networks have been previously used successfully to find either one or two dipoles sources. These approaches, however, have never solved the inverse problem in a distributed dipole model with more than two dipole sources. We present ConvDip, a novel convolutional neural network (CNN) architecture that solves the EEG inverse problem in a distributed dipole model based on simulated EEG data. We show that (1) ConvDip learned to produce inverse solutions from a single time point of EEG data and (2) outperforms state-of-the-art methods on all focused performance measures. It is more flexible when dealing with varying number of sources, produces less ghost sources and misses less real sources than the comparison methods. It produces plausible inverse solutions for real EEG recordings from human participants. (4) The trained network needs less than 40 ms for a single prediction. Our results qualify ConvDip as an efficient and easy-to-apply novel method for source localization in EEG data, with high relevance for clinical applications, e.g. in epileptology and real time applications.

neuroscience↗