Explore the vast range of titles available.

In this study we explore the interference rejection and spatial sampling properties of multi-axis Optically Pumped Magnetometer (OPM) data. We use both vector spherical harmonics and eigenspectra to quantify how well an array can separate neuronal signal from environmental interference while adequately sampling the entire cortex. We found that triaxial OPMs have superb noise rejection properties allowing for very high orders of interference (L=6) to be accounted for while minimally affecting the neural space (2dB attenuation for a 60-sensor triaxial system). We show that at least 11th order (143 spatial degrees of freedom) irregular solid harmonics or 95 eigenvectors of the lead field are needed to model the neural space for OPM data (regardless of number of axes measured). This can be adequately sampled with 75–100 equidistant triaxial sensors (225–300 channels) or 200 equidistant radial channels. In other words, ordering the same number of channels in triaxial (rather than purely radial) configuration may give significant advantages not only in terms of external noise rejection but also by minimizing cost, weight and cross-talk.

•Zero-field OPMs operate within a limited magnetic field range.•We correct for background field changes in real-time using coils on-board the OPMs.•We used a model of the background field based on low-order regular solid harmonics.•We were able to record auditory evoked fields during movements of 1.5m - 2m. Most neuroimaging techniques require the participant to remain still for reliable recordings to be made. Optically pumped magnetometer (OPM) based magnetoencephalography (OP-MEG) however, is a neuroimaging technique which can be used to measure neural signals during large participant movement (approximately 1 m) within a magnetically shielded room (MSR) (Boto et al., 2018; Seymour et al., 2021). Nevertheless, environmental magnetic fields vary both spatially and temporally and OPMs can only operate within a limited magnetic field range, which constrains participant movement. Here we implement real-time updates to electromagnetic coils mounted on-board of the OPMs, to cancel out the changing background magnetic fields. The coil currents were chosen based on a continually updating harmonic model of the background magnetic field, effectively implementing homogeneous field correction (HFC) in real-time (Tierney et al., 2021). During a stationary, empty room recording, we show an improvement in very low frequency noise of 24 dB. In an auditory paradigm, during participant movement of up to 2 m within a magnetically shielded room, introduction of the real-time correction more than doubled the proportion of trials in which no sensor saturated recorded outside of a 50 cm radius from the optimally-shielded centre of the room. The main advantage of such model-based (rather than direct) feedback is that it could allow one to correct field components along unmeasured OPM axes, potentially mitigating sensor gain and calibration issues (Borna et al., 2022).

•We explore multimodal activity generated by two different large-scale generative models.•Relevant dynamics across modalities emerge from delay-coupled gamma-band oscillators.•Local E-I balance supports the emergence of spatiotemporal network dynamics.•The omission of conduction delays dramatically decreases model performance.•The connectome imposes model-specific constraints on functional connectivity. Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome. To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears). Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.

There is renewed interest in electrical activity that extends beyond the typical electrophysiological 100 Hz bandwidth. This activity, often in the anterior temporal lobe, has been attributed to processes ranging from memory consolidation to epileptiform activity. Here, using an open-access resting state magnetoencephalography (MEG) dataset (n = 89), and a second task-based MEG dataset, we could reliably localise high-frequency power to the temporal lobes across multiple bands up to 300-400 Hz. A functional connectivity analysis of this activity revealed a robust resting state bilateral network between the temporal lobes. However, we also found robust coherence in the 100-200 and 200-300 Hz bands between source reconstructed MEG data and the electrooculography (EOG) localised to within the temporal poles. Additional denoising schemes applied to the data could reduce power localisation to the temporal poles but the topography of the functional network did not drastically alter. Whilst it is clear that this network is biological and robust to established denoising methods, we cannot definitively rule yet on whether this is of neural or myogenic origin.Competing Interest StatementThe authors have declared no competing interest.

Neuroimaging studies have typically relied on rigorously controlled experimental paradigms to probe cognition, in which movement is restricted, primitive, an afterthought or merely used to indicate a subject’s choice. Whilst powerful, these paradigms do not often resemble how we behave in everyday life, so a new generation of ecologically valid experiments are being developed. Magnetoencephalography (MEG) measures neural activity by sensing extracranial magnetic fields. It has recently been transformed from a large, static imaging modality to a wearable method where participants can move freely. This makes wearable MEG systems a prime candidate for naturalistic experiments going forward. However, these experiments will also require novel methods to capture and integrate information about complex behaviour executed during neuroimaging, and it is not yet clear how this could be achieved. Here we use video recordings of multi-limb dance moves, processed with open-source machine learning methods, to automatically identify analysis time windows of interest in concurrent wearable MEG data. In a first step, we compare a traditional, block-designed analysis of limb movements, where the times of interest are based on stimulus presentation, to an analysis pipeline based on hidden Markov model states derived from the video telemetry. Next, we show that it is possible to identify discrete modes of neuronal activity related to specific limbs and body posture by processing the participants’ choreographed movement in a dancing paradigm. This demonstrates the potential of combing video telemetry with mobile neuroimaging for future studies of complex and naturalistic behaviours.

Voluntary human movement relies on interactions between the spinal cord, brain, and sensory afferents. The integrative function of the spinal cord has proven particularly difficult to study directly and non-invasively in humans due to challenges in measuring spinal cord activity. Investigations of sensorimotor integration often rely on cortico-muscular coupling, which can capture interactions between the brain and muscle, but cannot reveal how the spinal cord mediates this communication. Here, we introduce a system for direct, non-invasive imaging of concurrent brain and cervical spinal cord activity in humans using optically-pumped magnetometers (OPMs). We used this system to study endogenous interactions between the brain, spinal cord, and muscle involved in sensorimotor control during simple maintained contraction. Participants (n=3) performed a hand contraction with real-time visual feedback while we recorded brain and spinal cord activity using OPMs and muscle activity using EMG. We first identify the part of the spinal cord exhibiting a peak in estimated current flow in the cervical region during contraction. We then demonstrate that rhythmic activity in the spinal cord exhibits significant coupling with both brain and muscle activity in the 5-35 Hz frequency range. These findings evidence the possibility of concurrent spatio-temporal imaging along the entire neuro-axis.

Current whole-brain models are generally tailored to the modelling of a particular modality of data (e.g., fMRI or MEG/EEG). Although different imaging modalities reflect different aspects of neural activity, we hypothesise that this activity arises from common network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct electromagnetic and metabolic features of brain activity to the dynamics on the macroscopic structural connectome. To jointly predict dynamical and functional connectivity features of distinct signal modalities, we consider two large-scale models generating local short-lived 40 Hz oscillations with various degrees of realism - namely Stuart Landau (SL) and Wilson and Cowan (WC) models. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC) and functional connectivity dynamics (FCD) to a comparable degree, by varying global coupling and mean conduction time delay. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD, highlighting the importance of balanced dynamics for the emergence of spatiotemporal patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.45 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns beyond the anatomical framework. Finally, we show that both models can generate MOMs with empirical-like properties. Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://gitlab.com/francpsantos/whole_brain_generative_models

In this study we explore the interference rejection and spatial sampling properties of multi-axis Optically Pumped Magnetometer (OPM) data. We use both vector spherical harmonics and eigenspectra to quantify how well an array can separate neuronal signal from environmental interference while adequately sampling the entire cortex. We found that triaxial OPMs have superb noise rejection properties allowing for very high orders of interference (L=6) to be accounted for while minimally affecting the neural space (2dB attenuation for a 60-sensor triaxial system). To adequately model the signals arising from the cortex, we show that at least 11th order (143 spatial degrees of freedom) irregular solid harmonics or 95 eigenvectors of the lead field are needed to model the neural space for OPM data (regardless of number of axes measured). This can be adequately sampled with 75-100 equidistant triaxial sensors (225-300 channels) or 200 equidistant radial channels. In other words, ordering the same number of channels in triaxial (rather than purely radial) configuration gives significant advantages not only in terms of external noise rejection but also minimizes cost, weight and cross-talk. Competing Interest Statement The authors have declared no competing interest.

Longitudinal CA125 algorithms are the current basis of ovarian cancer screening. We report on longitudinal algorithms incorporating multiple markers. In the multimodal arm of United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), 50,640 postmenopausal women underwent annual screening using a serum CA125 longitudinal algorithm. Women (cases) with invasive tubo-ovarian cancer (WHO 2014) following outcome review with stored annual serum samples donated in the 5 years preceding diagnosis were matched 1:1 to controls (no invasive tubo-ovarian cancer) in terms of the number of annual samples and age at randomisation. Blinded samples were assayed for serum human epididymis protein 4 (HE4), CA72-4 and anti-TP53 autoantibodies. Multimarker method of mean trends (MMT) longitudinal algorithms were developed using the assay results and trial CA125 values on the training set and evaluated in the blinded validation set. The study set comprised of 1363 (2–5 per woman) serial samples from 179 cases and 181 controls. In the validation set, area under the curve (AUC) and sensitivity of longitudinal CA125-MMT algorithm were 0.911 (0.871–0.952) and 90.5% (82.5–98.6%). None of the longitudinal multi-marker algorithms (CA125-HE4, CA125-HE4-CA72-4, CA125-HE4-CA72-4-anti-TP53) performed better or improved on lead-time. Our population study suggests that longitudinal HE4, CA72-4, anti-TP53 autoantibodies adds little value to longitudinal serum CA125 as a first-line test in ovarian cancer screening of postmenopausal women.

Stemming biodiversity loss requires greater investment in conservation and more efficient use of available resources. Prioritizing conservation actions that yield the most biodiversity benefit for the least cost can help maximize return on investment. Actions that have co‐benefits for other objectives, such as climate change mitigation, can also help mobilize additional funds for conservation. We used Priority Threat Management to identify actions to secure the greatest number of species groups of conservation concern for the least cost in the Lake Simcoe‐Rideau ecoregion, Ontario—one of Canada's biodiversity crisis ecoregions. We also estimated the carbon sequestration benefits of actions related to land protection and restoration. We found that without additional investment in conservation, 13 of 16 species groups were expected to have <50% probability of persistence in this ecoregion by 2050. Implementing all proposed strategies would yield the greatest biodiversity benefits and secure 12 of the 16 species groups with ≥60% probability of persistence, at a cost of CA $113 million per year over 27 years. In comparison, investing CA$ 97 million per year in landowner stewardship, habitat protection and restoration and regeneration strategies could secure 10 species groups and improve the probability of persistence of one additional group from 39% to 55%. The habitat protection and restoration strategies also deliver direct carbon benefits of around 11.2 Mt in total avoided CO2 emissions and 137.6 Mt CO2 in total potential sequestration, respectively, over the long‐term, thus supporting alignment with climate change mitigation targets and delivering co‐benefits that may further justify investment. Practical implication. By estimating the costs and demonstrating the expected benefits and potential carbon co‐benefits of conservation actions, Priority Threat Management can help maximize return on investment and identify actions that address multiple environmental crises. We worked with a diverse group of local experts to identify cost‐effective and complementary management strategies that could help secure the persistence of species of conservation concern for the least cost in the Lake Simcoe‐Rideau ecoregion of Southern Ontario. We found that without additional investment in conservation, 130 out of 133 species of conservation concern in the region could be lost by 2050. Investing up to $113 million more per year on strategies informed by local experts can help reverse this outcome, securing up to 100 species while also delivering significant co‐benefits for climate change mitigation objectives.