Biology subjects

O'Neill, G.

Publications and source records attributed to O'Neill, G..

7 recordsLinked to original sources

bioRxiv2026

Intracranial Validation of Magnetoencephalography Across Oscillatory Frequency and Depth

Non-invasive, whole-brain neuroimaging methods such as functional magnetic resonance imaging, electroencephalography (EEG), and magnetoencephalography (MEG) are essential tools for studying the basis of human cognition in health and disease. MEG offers the opportunity to study neural activity at its intrinsic timescale, by recording the magnetic fields generated by electrical currents within the brain from outside the skull. Moreover, recently developed optically pumped magnetometers (OPMs) allow these recordings to take place in new settings, for example during naturalistic behaviour and in previously inaccessible populations. These breakthroughs have led to a shift in the neuroimaging landscape, with a global increase in the adoption of MEG. Crucially, however, the extent to which MEG recordings can measure different features of neural activity remains unclear. To address this issue, we leveraged a unique and rare dataset of concurrent MEG and intracranial EEG recordings from a cohort of epileptic patients. We found that group-level inferences of spontaneous oscillatory dynamics made with source-localised MEG, i.e. estimates of power and bursts, accurately reflected the underlying neural activity. As expected, the agreement was strongest for lower-frequency activity (delta, theta, and alpha) and superficial sources, and weakest in the gamma range. Crucially, however, MEG was also sensitive to deep structures: it captured oscillatory power and burst dynamics in the hippocampus, most robustly in the theta band. These findings demonstrate that MEG is sensitive to physiologically meaningful activity in cortical and subcortical regions and establish a foundation for the interpretation of future MEG studies across a wide range of research domains.

neuroscience↗

bioRxiv2026

Forward Modelling for Magnetospinography: Systematic Comparison of Boundary Element and Finite Element Methods

Magnetospinography (MSG) enables non-invasive measurement of spinal cord electrophysiology, but accurate interpretation of these signals depends critically on forward modelling assumptions. We compared four vertebral bone representations: continuous, homogeneous-toroidal, inhomogeneous-toroidal, and MRI-derived-realistic, implemented within both boundary element method (BEM) and finite element method (FEM) frameworks. Lead-fields were computed across the full spinal cord for three orthogonal source orientations. When comparing matched model pairs, BEM and FEM produced consistent forward solutions, with median relative errors below 3.1% and median squared correlation coefficients exceeding 0.998 across all bone geometries. Vertebral bone geometry exerted a systematic and orientation-dependent influence on predicted lead fields. The dominant distinction was between continuous and segmented bone representations rather than between simplified and anatomically detailed segmented models. For left-right oriented sources, segmented geometries produced substantially higher field amplitudes (fT/nAm) than the continuous model throughout the cord (35-72%), while toroidal and MRI-derived realistic models produced comparable results across both frameworks, with median r2 exceeding 0.97 between segmented models compared to 0.67-0.85 for continuous versus segmented comparisons. Sensor placement further modulated sensitivity profiles, with posterior arrays exhibiting greater sensitivity lower down the cord, but anterior and posterior sensors showing comparable sensitivity in the cervical region.

neuroscience↗

bioRxiv2025

Intrinsic electrophysiological activity maps a latent dimension of poor sleep quality and reduced cognitive performance: a magnetoencephalography study using Cam-CAN data.

Sleep quality and cognition vary as functions of lifestyle, genetics, and health. However, poor sleep quality is prevalent, reportedly affecting approximately 38% of the adult population. Poor sleep quality is a significant risk factor for mood disorders, a predictive factor in cognitive decline in later life, and sleep disturbance is a putative precursor to severe cognitive impairment and is predictive of dementia onset. The direct relationship between these factors and intrinsic neural function is poorly understood. Integrating neurophysiology, cognition, and sleep quality to reveal latent factors would help understand the neurophysiology of sleep disturbances and their relation to cognitive performance. Here, we used data from the Cam-CAN dataset with a partial least squares (PLS) approach, producing a multivariate cross-decomposition model to map resting-state magnetoencephalography (MEG) data and cognitive/sleep scores of healthy controls (n = 490, age 18-86). Normative modelling was applied to MEG data, correcting for effects of age, sex, and handedness. We identify a significant relationship between poorer self-reported sleep quality and lower cognitive performance across multiple domains, characterised by excess low-frequency neural activity and reduced high-frequency activity, particularly in the alpha band. Moreover, globally increased low frequency and decreased alpha-beta functional connectivity - the dominant frequency channel in neural connectivity at rest across the brain - contribute significantly to this relationship. Our multivariate mapping approach enabled us to parse the electrophysiological signatures directly related to sleep quality and cognitive performance, independent of the confounding effects of age, and show that neural slowing and dysconnectivity are intrinsically linked to self-reported sleep quality and cognitive performance.

neuroscience↗

bioRxiv2023

Combining Magnetoencephalography with Telemetric Streaming of Intracranial Recordings and Deep Brain Stimulation - a Feasibility Study

The combination of subcortical Local Field Potential (LFP) recordings and stimulation with Magnetoencephalography (MEG) in Deep Brain Stimulation (DBS) patients enables the investigation of cortico-subcortical communication patterns and provides insights into DBS mechanisms. Until now, these recordings have been carried out in post-surgical patients with externalised leads. However, a new generation of telemetric stimulators makes it possible to record and stream LFP data in chronically implanted patients. Nevertheless, whether such streaming can be combined with MEG has not been tested. In the present study, we tested the most commonly implanted telemetric stimulator - Medtronic Percept PC with a phantom in three different MEG systems: two cryogenic scanners (CTF and MEGIN) and an experimental Optically Pumped Magnetometry (OPM)-based system. We found that when used in combination with the new SenSight segmented leads, Percept PC telemetric streaming only generates band-limited interference in the MEG at 123Hz and harmonics. However, the legacy streaming mode used with older lead models generates multiple, dense artefact peaks in the physiological range of interest (below 50Hz). The effect of stimulation on MEG critically depends on whether it is done in bipolar (between two contacts on the lead) or monopolar (between a lead contact and the stimulator case) mode. Monopolar DBS creates severe interference in the MEG as previously reported. However, we found that the OPM system is more resilient to this interference and could provide artefact-free measurements at least for limited frequency ranges. A resting measurement in the MEGIN system from a Parkinsons patient implanted with Percept PC and subthalamic SenSight leads revealed artefact patterns consistent with our phantom recordings. Moreover, analysis of LFP-MEG coherence in this patient showed oscillatory coherent networks consistent in their frequency and topography with those described in published group studies done with externalised leads. In conclusion, Percept PC telemetric streaming with SenSight leads is compatible with MEG. Furthermore, OPM sensors could provide additional new opportunities for studying DBS effects.

neuroscience↗

bioRxiv2023

Parsing the effect of co-culture with brain organoids on Diffuse Intrinsic Pontine Glioma (DIPG) using quantitative proteomics

Diffuse Intrinsic Pontine Gliomas (DIPGs) are deadly brain cancers in children for which there is currently no effective treatment. This can partly be attributed to preclinical models that lack essential elements of the in vivo tissue environment, resulting in treatments that appear promising preclinically, but fail to result in effective cures. Recently developed co-culture models combining stem cell-derived brain organoids with brain cancer cells provide tissue dimensionality and a human-relevant tissue-like microenvironment. As these models are technically challenging and time consuming it is imperative to establish whether interaction with the organoid influences DIPG biology and thus warrants their use. To address this question, we cultured DIPG cells with cortical organoids. We created "mosaic" co-cultures enriched for tumour cell-neuronal cell interactions versus "assembloid" co-cultures enriched for tumour cell-tumour cell interactions. Sequential window acquisition of all theoretical mass spectra (SWATH-MS) was used to analyse the proteomes of DIPG fractions isolated by flow-assisted cell sorting. Control proteomes from DIPG spheroids were compared with DIPG cells isolated from mosaic and assembloid co-cultures. This revealed that tumour cell adhesion was reduced, and DNA synthesis and replication were increased, in DIPG cells under either co-culture condition. By contrast, the mosaic co-culture was associated with pathways implicated in dendrite growth. We propose that co-culture with brain organoids is a valuable tool to parse the contribution of the brain microenvironment to DIPG tumour biology.

cancer biology↗

bioRxiv2022

Spatiotemporal organization of human sensorimotor beta burst activity

Beta oscillations in human sensorimotor cortex are hallmark signatures of healthy and pathological movement. In single trials, beta oscillations include bursts of intermittent, transient periods of high-power activity. These burst events have been linked to a range of sensory and motor processes, but their precise spatial, spectral, and temporal structure remains unclear. Specifically, a role for beta burst activity in information coding and communication suggests spatiotemporal patterns, or travelling wave activity, along specific anatomical gradients. We here show in human magnetoencephalography recordings that burst activity in sensorimotor cortex occurs in planar spatiotemporal wave-like patterns that dominate along two axes either parallel or perpendicular to the central sulcus. Moreover, we find that the two propagation directions are characterised by distinct anatomical and physiological features. Finally, our results suggest that sensorimotor beta bursts occurring before and after a movement share the same generator but can be distinguished by their anatomical, spectral and spatiotemporal characteristics, indicating distinct functional roles.

neuroscience↗

bioRxiv2019

Detecting M/EEG modular brain states in rest and task

The human brain is a dynamic networked system that continually reconfigures its connectivity patterns over time. Thus, developing approaches able to adequately detect fast brain dynamics is critical. Of particular interest are the methods that analyze the modular structure of brain networks, i.e. the presence of clusters of regions that are densely inter-connected. In this paper, we propose a novel framework to identify fast modular states that dynamically fluctuate over time during rest and task. We validated our method using MEG data recorded during a finger movement task, identifying modular states linking somatosensory and primary motor regions. The algorithm was also validated on dense-EEG data recorded during picture naming task, revealing the sub-second transition between several modular states which relate to visual processing, semantic processing and language. Next, we validated our method on a dataset of resting state dense-EEG signals recorded from 124 patients with Parkinson disease and different cognitive phenotypes. Results disclosed brain modular states that differentiate cognitively intact patients, patients with moderate cognitive deficits and patients with severe cognitive deficits. Our new approach tracks the brain modular states, in healthy subjects and patients, on an adequate task-specific timescale.

neuroscience↗