Biology subjects

von Ellenrieder, N.

Publications and source records attributed to von Ellenrieder, N..

6 recordsLinked to original sources

bioRxiv2026

The electro-MICA toolbox for integrating electrophysiology within multimodal imaging and connectomics workflows

AO_SCPLOWBSTRACTC_SCPLOWThe integration of electrophysiological recordings with multimodal neuroimaging data holds great promise for advancing our understanding of brain function and neurological disorders. To facilitate this endeavour, we present electro-MICA, an open-access Python toolbox designed to project electrophysiological features from scalp and intracranial electroencephalography (EEG) onto cortical and hippocampal surfaces generated by validated multimodal imaging ecosystems. The toolbox comprises two pipelines: one for intracranial EEG (iEEG) recorded with stereo-EEG depth electrodes, and one for scalp EEG source localization. Both pipelines are grounded in numerical solutions to the electromagnetic equations governing electric activity in the brain, solved using the Boundary Element Method. A key methodological contribution is the use of a current density double layer model for neural generators, which avoids the mathematical singularities introduced by conventional dipole-based models when electrodes are near the cortical surface, a situation that can arise in iEEG. Electrode contacts are additionally modeled with non-zero length, improving physical realism. Scalp EEG source localization is performed using eLORETA on a subject-specific three-layer head model derived from the anatomical input. Validation against empirical gamma-band iEEG data from 32 subjects demonstrates that the distributed generator model outperforms both distance-based and dipole-based alternatives. An illustrative clinical example demonstrates the toolboxs capacity to reveal associations between intracranial spike rates, cortical thickness, and anatomical connectivity in an epilepsy patient. Electro-MICA requires no parameter selection from the user, facilitating straightforward multimodal analyses in both research and clinical settings. The toolbox is available at github.com/MICA-MNI/electromica with extensive online documentation at electromica.readthedocs.io.

neuroscience↗

bioRxiv2026

Pathology in resected areas of FDG PET hypometabolism in pediatric epilepsy patients with focal cortical dysplasia

Introduction[18F]fluorodeoxyglucose positron emission tomography (FDG-PET) frequently reveals hypometabolism extending beyond the epileptogenic zone in focal cortical dysplasia (FCD). However, it is unclear whether these peripheral hypometabolic areas harbour pathological cells potentially contributing to seizure generation. This study characterized histopathology in the lesion epicentre vs borders of the FDG-PET hypometabolism-informed resections in pediatric patients undergoing epilepsy surgery. MethodsFourteen children with intractable, extra-temporal focal epilepsy (mean age 9.0{+/-}5.0 years; 9 female) were retrospectively reviewed. FDG-PET contributed significantly to surgical planning in all cases, with the resection encompassing the visually-apparent MRI signal abnormalities as well as areas of surrounding hypometabolism when safely feasible. Multiple pathological specimens were obtained from the epicentre and surrounding hypometabolic areas. Overall, 136 specimens were analyzed: 64 epicentre (mean 4.6{+/-}3.2/patient) and 72 border (mean 5.1{+/-}3.5/patient). ResultsPathology was identified in 75% of epicentre specimens (59% with frank FCD (fFCD) IIa/b, 16% with dysmorphic neurons only (DNO)). Border specimens showed pathology in 62% (31% fFCD IIa/b, 31% DNO). We fitted a Bayesian logistic mixed model with pathology as outcome variable, location as predictor, and subject as a random effect. Compared to negative pathology, the log-odds of fFCD in the epicentre was 1.00 (confidence interval (CI) 0.32, 1.77) and -1.25 in the border (CI -2.17, -0.40). The log-odds of DNO vs negative pathology was non-significant in both locations. All patients achieved Engel Ia status at one-year follow-up with no long-term neurological deficits. ConclusionThese findings suggest a gradient of histopathology, with fFCD concentrated in the epicentre and DNO present in both the epicentre and hypometabolic borders. Thus, FDG-PET may be used to better detect the histopathological borders of FCD type II, and the high seizure-freedom rate presented here supports the inclusion of these surrounding hypometabolic regions in the surgical resection (when safe to do so), potentially improving the likelihood of removing epileptogenic cells. Key PointsO_LIPathological cells are present not only in the MRI signal abnormality in FCD but also in the periphery of the FDG-PET hypometabolism. C_LIO_LIWe observe a gradient of histopathology, with frank FCD concentrated in the epicentre and dysmorphic neurons spread throughout the area of hypometabolism. C_LIO_LIMaximal safe resection of the area of hypometabolism may increase likelihood of removing epileptogenic cells, thus improving surgical outcome. C_LI

neuroscience↗

bioRxiv2025

The Hemodynamic Response Function Varies Across Anatomical Location and Pathology in the Epileptic Brain

The hemodynamic response function (HRF) links neuronal activity to functional magnetic resonance imaging (fMRI) signals. While most fMRI studies use a "canonical" HRF, increasing evidence from studies of healthy subjects suggests that the HRF depends on anatomical location and disease states. Here, we investigate how HRF variability relates to anatomical location and pathology in the epileptic brain, using a large simultaneous electroencephalogram and fMRI dataset. Applying HRF deconvolution and temporal decomposition, we built the first whole-brain HRF library specific to epilepsy, identifying four distinct shape groups. We mapped HRF features across parcellations of two atlases using novel Bayesian hierarchical models. In non-epileptogenic regions, HRF shape and spatial distributions align with findings from healthy subjects. Within pathological regions, they vary significantly according to pathology. Our results indicate that HRF variability is associated with pathology, in addition to its dependence on anatomical location, motivating region- and pathology-based HRF modulation in epilepsy studies.

neuroscience↗

bioRxiv2022

Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas

BackgroundMagnetoencephalography (MEG) is a widely used non-invasive tool to estimate brain activity with high temporal resolution. However, due to the ill-posed nature of the MEG source imaging (MSI) problem, the ability of MSI to identify accurately underlying brain sources along the cortical surface is still uncertain and requires validation. MethodWe validated the ability of MSI to estimate the background resting state activity of 45 healthy participants by comparing it to the intracranial EEG (IEEG) atlas (https://mni-open-ieegatlas.research.mcgill.ca/). First, we applied wavelet-based Maximum Entropy on the Mean (wMEM) as an MSI technique. Next, we converted MEG source maps into intracranial space, by applying a forward model to the MEG reconstructed source maps and estimated virtual IEEG (VIEEG) potentials on each IEEG channel location and quantitatively compared those with actual IEEG signals from the atlas for 38 regions of interest in the canonical frequency bands. ResultsThe MEG spectra were more accurately estimated in the lateral regions compared to the medial regions. The regions with higher amplitude in the VIEEG than in the IEEG were more accurately recovered. In the deep regions, MEG estimated amplitudes were largely underestimated and the spectra were poorly recovered. Moreover, the MEG largely overestimated oscillatory peaks in the alpha band, especially in the anterior and deep regions. This is possibly due to higher phase synchronization of alpha oscillations over extended regions, exceeding the spatial sensitivity of IEEG but detected by MEG. Importantly, we found that MEG estimated spectra were more comparable to spectra from the IEEG atlas after the aperiodic components were removed. ConclusionThis study identifies brain regions and frequencies for which MEG source analysis is likely to be reliable, a promising step towards resolving the uncertainty in recovering intracerebral activity from non-invasive MEG studies. HighlightsO_LIValidation of MEG source imaging with intracranial EEE atlas C_LIO_LIAssessment of resting state human brain oscillations from healthy brain C_LIO_LIAdapted source imaging method, wMEM, to localize resting state oscillations C_LIO_LIIdentified brain regions with oscillations accurately estimated by MEG C_LIO_LIMEG estimated spectra dominated by oscillations in the alpha band C_LI

neuroscience↗

bioRxiv2020

Fast oscillations localize the epileptogenic zone: an electrical source imaging study using high-density electroencephalography

ObjectiveFast Oscillations (FO) >40 Hz are a promising biomarker of the epileptogenic zone (EZ). Evidence using scalp electroencephalography (EEG) remains scarce. We assessed if electrical source imaging of FO using 256-channel high-density EEG (HD-EEG) is useful for EZ identification. MethodsWe analyzed HD-EEG recordings of 10 focal drug-resistant epilepsy patients with seizure-free postsurgical outcome. We marked FO candidate events at the time of epileptic spikes and verified them by screening for an isolated peak in the time-frequency plot. We performed electrical source imaging of spikes and FO within the Maximum Entropy of the Mean framework. Source localization maps were validated against the surgical cavity. ResultsWe identified FO in five out of 10 patients who had a superficial or intermediate deep generator. The maximum of the FO maps was localized inside the cavity in all patients (100%). Analysis with a reduced electrode coverage using the 10-10 and 10-20 system showed a decreased localization accuracy of 60% and 40% respectively. ConclusionsFO recorded with HD-EEG localize the EZ. HD-EEG is better suited to detect and localize FO than conventional EEG approaches. SignificanceThis study acts as proof-of-concept that FO localization using 256-channel HD-EEG is a viable marker of the EZ. Highlights- Fast oscillations > 40Hz are able to correctly localize the epileptogenic zone. - HD-EEG is superior in detection and localization of fast oscillations compared to conventional EEG approaches. - Presence of fast oscillations on the scalp might point to a superficial epileptic generator.

neuroscience↗

bioRxiv2018

Identification negative BOLD responses using windkessel models

Alongside positive BOLD responses (PBR), a variety of negative BOLD responses (NBR) with distinct underlying mechanisms also occur. We identify five mechanisms of NBR: i) local/lateral/contralateral inhibition (LCI), ii) neuronal disruption of network activity (NDA), iii) altered balance of neuro-metabolic/vascular couplings (ANC), iv) arterial blood stealing (ABS), and v) venous blood backpressure (VBB). Detecting and classifying these mechanisms from BOLD signals is pivotal in understanding normal/pathological brain functions. This requires models and parameters with anatomical/functional interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a windkessel model with viscoelastic compliance as well as dynamics of both neuronal and tissue/blood O2 to investigate the generation, detection, classification and interpretation of the BOLD hemodynamic response functions (HRF) of above mechanisms. Firstly, we evaluated the use of the general linear model to detect simulated NBRs. Secondly, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying a new HRF. Crossvalidation indicates NDA and ANC can accurately be classified solely from fMRI BOLD signals; while LCI, ABS and VBB might require additional imaging modalities. Thirdly, we demonstrated that estimators of the model parameters determinant in the NBRs formation are accurate, and precise to certain resolutions. Finally, we successfully applied our detection/classification/estimation methodology to EEG-fMRI data in a clinical situation where several of these mechanisms could coexist. We believe that the proper identification and interpretation of NBR mechanisms have important clinical and cognitive implications in fMRI studies.

neuroscience↗