Biology subjects

Mosher, J. C.

Publications and source records attributed to Mosher, J. C..

6 recordsLinked to original sources

bioRxiv2026

Biophysical modeling reveals how recording geometry shapes representations of cortical activity

Extracellular electrophysiological recordings reflect both the organization of neural activity and the geometry through which that activity is sampled. Here, we used biophysical modeling and experimental recordings from the macaque auditory cortex to determine how cortical activity is transformed across neural sources, extracellular signals, and recording geometries. In a biophysically detailed thalamocortical model, four separately instantiated cortical-column conditions spanning graded thalamic recruitment exhibited a graded response hierarchy across local field potential, current-source density, and multiunit activity, despite signal-dependent differences in the contributions of cortical neuronal populations. We then arranged independently simulated cortical columns into controlled spatial configurations and sampled the same underlying sources using virtual linear laminar and Directional and Scalable (DiSc) electrode arrays. The linear geometry primarily represented variation along cortical depth, whereas DiSc additionally represented circumferential variation associated with lateral source organization, demonstrating that recording geometry determines which spatial dimensions of common underlying activity are represented in extracellular measurements. Experimental recordings obtained with the corresponding geometries were consistent with these model-derived predictions: broadband-noise responses exhibited spatial organization shared across tone-evoked responses in both multidimensional DiSc maps and laminar profiles, despite substantial variation in frequency-dependent response magnitude across penetrations. Together, these results show that extracellular measurements represent an interaction between biological source organization and recording geometry. Biophysical forward modeling therefore provides a framework for determining which features of underlying circuit organization remain identifiable after extracellular sampling and for relating neural-interface design to the dimensions of cortical activity accessible experimentally.

neuroscience↗

bioRxiv2026

Microelectrode arrays enable directional stereo-EEG during kainate-mediated seizures

Surgical planning for drug-resistant epilepsy often relies on stereo-EEG (sEEG) recordings obtained with cylindrical ring electrodes. Prior modeling studies, including lead-field analysis, suggest that microelectrodes distributed around an sEEG-sized insulating body offer superior source amplification and directional sensitivity that are not available with either a ring design or microelectrodes on micro-structures, e.g. Neuropixel. However, these advantages have not been demonstrated in seizure models. This study evaluated high-density sEEG recordings using directional microelectrode arrays in a kainate-mediated rat model (n=6). Two 64-channel microelectrode arrays were implanted near the hippocampus, and the signals were spatially averaged to emulate virtual ring electrodes for comparison. Device locations were reconstructed and placed in copies of the Waxholm Space Rat Brain Atlas registered to subject-specific MRI scans. In subjects exhibiting seizures (n=4), automated line length detection showed that microelectrode signals identified epileptiform activity sooner and with greater specificity than ring electrodes. In subjects that only seized post-kainate injection (n=3), manual review by a board-certified epileptologist confirmed that microelectrodes provided the earliest onset detection times. Furthermore, the microelectrode arrays high resolution revealed distinct instances of hyperactivity occurring at similar depths but from different directions -- a feature indistinguishable to standard ring electrodes. These results demonstrate that microelectrodes on a large insulating body significantly enhance signal quality and spatial localization. This technology offers a potential advancement over current clinical standards for identifying seizure foci during surgical planning. SIGNIFICANCESurgical treatment for drug-resistant epilepsy depends on accurately identifying the brain regions where seizures begin. Current stereo-EEG electrodes sample activity with ring contacts that average signals around the probe shaft, potentially obscuring directional differences in nearby neural activity. This study shows that microelectrodes distributed around an sEEG-sized insulating body can improve seizure-related signal detection and spatial localization compared with ring-like recordings from the same implant locations. By resolving activity that conventional ring electrodes cannot distinguish, high-density directional sEEG may provide more informative recordings for seizure mapping. These findings support the development of next-generation intracranial electrodes for improving epilepsy surgical planning.

neuroscience↗

bioRxiv2025

sEEG-Suite: An Interactive Pipeline for Semi-Automated Contact Localization and Anatomical Labeling with Brainstorm

Stereoelectroencephalography (sEEG) is a critical tool for mapping epileptic networks in patients with drug-resistant epilepsy. Accurate localization and labeling of sEEG contacts are essential for identifying the seizure onset zone (SOZ) and ensuring optimal resective surgery. Traditional methods for localizing and labeling sEEG contacts rely on manual processing, which is prone to human error and variability. To address these challenges, we developed and integrated a semi-automatic sEEG contact localization and labeling pipeline within Brainstorm1, an open-source software platform for multimodal brain imaging analysis2-8, widely adopted in the neuroscience community with over 50,000 registered users and an active online user forum. The software has been supported by the National Institute of Health (NIH) for over two decades. The pipeline presented in this paper performs three key steps: (1) import and apply rigid co-registration of post-implantation Computed Tomography (CT) or post-CT with pre-implantation Magnetic Resonance Imaging (pre-MRI), (2) post-CT image segmentation and semi-automatic detection of sEEG contacts using GARDEL9, which has been integrated as a Brainstorm plugin, and (3) automatic anatomical labeling of contacts using standard and commonly used brain anatomy templates and atlases. Integrating this pipeline into Brainstorm brings the best of both worlds: GARDELs automation and Brainstorms multimodal data compatibility, and rich library of visualization and advanced analysis tools at both sensor and source level6. This sEEG-Suite tool facilitates reproducible research, supports clinical workflows, and accelerates sEEG-based investigations of invasive brain recordings.

neuroscience↗

bioRxiv2023

Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline

An expansive area of research focuses on discerning patterns of alterations in functional brain networks from the early stages of Alzheimers disease, even at the subjective cognitive decline (SCD) stage. Here, we developed a novel hyperbolic MEG brain network embedding framework for transforming high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations. Using this model, we computed hyperbolic embeddings of the MEG brain networks of two distinct participant groups: individuals with SCD and healthy controls. We demonstrated that these embeddings preserve both local and global geometric information, presenting reduced distortion compared to rival models, even when brain networks are mapped into low-dimensional spaces. In addition, our findings showed that the hyperbolic embeddings encompass unique SCD-related information that improves the discriminatory power above and beyond that of connectivity features alone. Notably, we introduced a unique metric--the radius of the node embeddings--which effectively proxies the hierarchical organization of the brain. Using this metric, we identified subtle hierarchy organizational differences between the two participant groups, suggesting increased hierarchy in the dorsal attention, frontoparietal, and ventral attention subnetworks among the SCD group. Last, we assessed the correlation between these hierarchical variations and cognitive assessment scores, revealing associations with diminished performance across multiple cognitive evaluations in the SCD group. Overall, this study presents the first evaluation of hyperbolic embeddings of MEG brain networks, offering novel insights into brain organization, cognitive decline, and potential diagnostic avenues of Alzheimers disease.

bioinformatics↗

bioRxiv2021

The Listening Zone of Human Electrocorticographic Field Potential Recordings

Intracranial electroencephalographic (icEEG) recordings provide invaluable insights into neural dynamics in humans due to their unmatched spatiotemporal resolution. Yet, such recordings reflect the combined activity of multiple underlying generators, confounding the ability to resolve spatially distinct neural sources. To empirically quantify the listening zone of icEEG recordings, we computed the correlations between signals as a function of distance (expressed as full width at half maximum; FWHM) between 8,752 recording sites in 71 patients implanted with either subdural electrodes (SDE), stereo-encephalography electrodes (sEEG), or high-density sEEG electrodes. As expected, for both SDE and sEEG electrodes, higher frequency signals exhibited a sharper fall off relative to lower frequency signals. For broadband high gamma (BHG) activity, the mean FWHM of SDEs (6.6 {+/-} 2.5 mm) and sEEGs in gray matter (7.14 {+/-} 1.7 mm) was not significantly different, however the FWHM for low frequencies recorded by sEEGs was 2.45 mm smaller than SDEs. White matter sEEG electrodes showed much lower power for frequencies 17 to 200 Hz (q < 0.01) and a much broader decay (11.3 {+/-} 3.2 mm) than gray matter electrodes (7.14 {+/-} 1.7 mm). The use of a bipolar referencing scheme significantly lowered FWHM for sEEG electrodes, as compared with a white matter reference or a common average reference. These results outline the influence of array design, spectral bands, and referencing schema on local field potential recordings and source localization in icEEG recordings in humans. The metrics we derive have immediate relevance to the analysis and interpretation of both cognitive and epileptic data.

neuroscience↗

bioRxiv2019

Comparison of beamformer implementations for MEG source localization

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (FieldTrip, SPM12, Brainstorm, and MNE-Python) using datasets both with and without SSS interference suppression.\n\nWe analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with SNR in all four toolboxes.\n\nWhen applied carefully to MEG data with a typical SNR (3-15 dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs. We also found that the SNR improvement offered by SSS led to more accurate localization.

neuroscience↗