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Mellor, S.

Publications and source records attributed to Mellor, S..

13 recordsLinked to original sources

Perceived Control Over Breathing Perturbations Depends on Interosensory Input and Prediction Error

Interoception and homeostatic/allostatic control are not only fundamental for survival but play a key role for maintaining somatic and mental health. Furthermore, metacognitive evaluations of these processes, such as perceived failure of allostatic regulation, have been proposed to underlie the experience of chronic symptoms, such as chronic fatigue. A central question in this regard is what mechanisms the brain uses to evaluate control over bodily states. A prominent computational proposal posits that this is achieved by monitoring of interoceptive prediction errors (PEs). However, to date, this proposal has not been tested experimentally. Here, we tested this hypothesis by applying computational process models of perceived control to data from a novel behavioural paradigm, the Respiratory Metacognition of Control Task (RMCT). The RMCT manipulates control over breathing by changing inspiratory resistive loads as a function of control achieved in a gamified prediction task. We developed and compared trial-by-trial generative models of perceived explicit control in the RMCT, using data from 50 volunteers in a pre-registered analysis. Bayesian model selection suggested that perceived control over breathing is best explained as a function of both trial-wise interoceptive outcomes (breathing with or without an inspiratory resistive load) and trial-wise PEs about respiratory resistance. These results support a longstanding computational proposal of how the brain detects failures of bodily regulation and provide a mechanistic model for understanding inter-individual differences in perceived control over bodily states.

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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.

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Test-retest reliability of auditory MMN measured with OPM-MEG

In this paper, we report results from an investigation of auditory mismatch responses as measured by magnetoencephalography (MEG) based on optically pumped magnetometers (OPM). Specifically, as part of a quality control study, we examined the reliability and validity of auditory mismatch negativity (MMN) recordings, obtained with a newly installed OPM-MEG system. Based on OPM-MEG data from 30 healthy volunteers, measured twice with an established auditory MMN paradigm with frequency deviants, we examined the following questions: First, we focused on construct validity and examined whether OPM-MEG measurements of MMN responses (in terms of event-related fields, ERFs) were qualitatively comparable to previous MMN findings from studies using EEG or MEG based on superconducting quantum interference devices (SQUIDs). In particular, we examined whether significant MMN responses measured by OPM-MEG occurred in a comparable time window and showed a similar topography as in previous EEG/MEG studies of MMN. Second, we quantified test-retest reliability of MMN amplitude and latency over two separate measurement sessions. The results of our analyses show that MMN responses recorded with OPM-MEG are in good agreement with previously reported MMN results in terms of timing and topography. Furthermore, the comparison of group-level MMN topographies and timeseries shows excellent consistency across the two measurement sessions. Our quantitative test-retest reliability analyses at the sensor level indicate good reliability for MMN amplitude, but poor reliability for MMN latency. Overall, our findings suggest that OPM-MEG measurements of auditory MMN (i) are comparable to results from EEG and SQUID-based MEG and (ii) show good test-retest reliability for amplitude measures at the sensor level. Notably, these results were achieved in an "out of the box" state of the OPM-MEG system, shortly after installation and without further optimisation. The reason for the insufficient reliability for MMN latency we observed is currently under investigation and represents an important target for future improvements.

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Volume conductor models for magnetospinography

The recent development of small, wearable, magnetic field sensors allow for the investigation of biomagnetic fields with a flexibility previously unavailable. We carry out forward computations to describe how current flow in the spinal cord and thorax gives rise to measurable magnetic fields outside the torso. We compare various open-access volume conductor models, in order to select the most parsimonious and accurate descriptor of the magnetic fields due to source current in the spinal cord. We find that fields produced due to current flow along the superior-inferior axis of the cord are relatively insensitive to the choice of volume conductor model. However, fields produced by current flow in predominantly left-right or anterior-posterior direction are significantly attenuated by the presence of bone in the forward model. Furthermore, volume conductors with bone demonstrate larger differences in field topographies for nearby sources compared to bone-free models. These findings suggest that precise modelling of spinal cord location and surrounding vertebrae will be important a-priori knowledge going forward.

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Adaptive multipole models of OPM data.

Multipole expansions have been used extensively in the Magnetoencephalography (MEG) literature for mitigating environmental interference and modelling brain signal. However, their application to Optically Pumped Magnetometer (OPM) data is challenging due to the wide variety of existing OPM sensor and array designs. We therefore explore how such multipole models can be adapted to provide stable models of brain signal and interference across OPM systems. Firstly, we demonstrate how prolate spheroidal (rather than spherical) harmonics can provide a compact representation of brain signal when sampling on the scalp surface with as few as 100 channels. We then introduce a type of orthogonal projection incorporating this basis set, the Adaptive Multipole Models (AMM), which provides robust interference rejection across systems, even in the presence of spatially structured nonlinearity errors. This projection is always stable, as it is an orthogonal projection, and will only ever decrease the white noise in the data. However, for array designs that are suboptimal for spatially separating brain signal and interference, this method can remove brain signal components. We contrast these properties with the more typically used multipole expansion, Signal Space Separation (SSS), which never reduces brain signal amplitude but is less robust to the effect of sensor nonlinearity errors on interference rejection and can increase noise in the data if the system is sub-optimally designed (as it is an oblique projection). We conclude with an empirical example utilizing AMM to maximize signal to noise ratio (SNR) for the stimulus locked neuronal response to a flickering visual checkerboard in a 128-channel OPM system and demonstrate up to 40dB software shielding in real data.

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Combining video telemetry and wearable MEG for naturalistic imaging

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 subjects 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.

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Is high-frequency activity evidence of an anterior temporal lobe network or micro-saccades?

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.

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Neural activity in human eyeblink conditioning: an optically pumped magnetometer-based MEG study

There is a profound lack of electrophysiological data from the cerebellum in humans, as compared to animals, because the pervading perception is that it is difficult to record human cerebellar activity non-invasively using magnetoencephalography (MEG) or electroencephalography (EEG). We used on-scalp MEG based on optically pumped magnetometers (OP-MEG) to detect learning related cerebellar signals in a classical eyeblink conditioning paradigm. In four healthy human adults, we observed unconditioned stimulus-related evoked responses locating in the cerebellum. These evoked responses diminished during the acquisition of conditioned responses, which corresponds with previous observed changes of Purkinje cell activities in animals. We also observed evoked responses immediately before conditioned blinks in 3 out of 4 participants, which were also located in the cerebellum. We discuss the potential nature of these cerebellar evoked responses and future applications of OP-MEG to the studies of human cerebellar physiology.

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Spherical harmonic based noise rejection and neuronal sampling with multi-axis OPMs

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.

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Testing covariance models for MEG source reconstruction of hippocampal activity

Beamforming is one of the most commonly used source reconstruction methods for magneto- and electroencephalography (M/EEG). One underlying assumption, however, is that distant sources are uncorrelated and here we tested whether this is an appropriate model for the human hippocampal data. We revised the Empirical Bayesian Beamfomer (EBB) to accommodate specific a-priori correlated source models. We showed in simulation that we could use model evidence (as approximated by Free Energy) to distinguish between different correlated and uncorrelated source scenarios. Using group MEG data in which the participants performed a hippocampal-dependent task, we explored the possibility that the hippocampus or the cortex or both were correlated in their activity across hemispheres. We found that incorporating a correlated hippocampal source model significantly improved model evidence. Our findings help to explain why, up until now, the majority of MEG-reported hippocampal activity (typically making use of beamformers) has been estimated as unilateral.

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Modelling optically pumped magnetometerinterference as a mean (magnetic) field

Here we propose that much of the magnetic interference observed when using optically pumped magnetometers can be modeled spatially as a mean (magnetic) field. We show that this approximation reduces sensor level variability and substantially improves statistical power. This model does not require knowledge of the underlying neuroanatomy nor the sensor positions. It only needs information about the sensor orientation. Due to the models low rank there is little risk of removing substantial neural signal. However, we provide a framework to assess this risk for any sensor number, design or subject neuroanatomy. We find that the risk of unintentionally removing neural signal is reduced when multi-axis recordings are performed. We validated the method using a binaural auditory evoked response paradigm and demonstrated that the mean field correction increases reconstructed SNR in relevant brain regions in both the spatial and temporal domain. Considering the models simplicity and efficacy, we suggest that this mean field correction can be a powerful preprocessing step for arrays of optically pumped magnetometers.

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Mouth magnetoencephalography: A unique perspective on the human hippocampus

Traditional magnetoencephalographic (MEG) brain imaging scanners consist of a rigid sensor array surrounding the head; this means that they are maximally sensitive to superficial brain structures. New technology based on optical pumping means that we can now consider more flexible and creative sensor placement. Here we explored the magnetic fields generated by a model of the human hippocampus not only across scalp but also at the roof of the mouth. We found that simulated hippocampal sources gave rise to dipolar field patterns with one scalp surface field extremum at the temporal lobe and a corresponding maximum or minimum at the roof of the mouth. We then constructed a fitted dental mould to accommodate an Optically Pumped Magnetometer (OPM). We collected data using a previously validated hippocampal-dependent task to test the empirical utility of a mouth-based sensor, with an accompanying array of left and right temporal lobe OPMs. We found that the mouth sensor showed the greatest task-related theta power change. We also found that, as predicted by the simulations, the mouth sensor was anti-correlated with those on over the temporal lobes. We found that this sensor had a mild effect on the reconstructed power in the hippocampus (~10% change) but that coherence images between the mouth sensor and reconstructed source images showed a global maximum in the right hippocampus. We conclude that augmenting a scalp-based MEG array with sensors in the mouth shows unique promise for both basic scientists and clinicians interested in interrogating the hippocampus.

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Pragmatic spatial sampling for wearable MEG arrays

Several new technologies have recently emerged promising new MEG systems in which the sensors can be placed close to the scalp. One such technology, Optically Pumped Magnetometry MEG (OP-MEG) allows for a scalp mounted flexible system that provides field measurements within mm of the scalp surface. A question that arises in developing on-scalp systems, such as OP-MEG scanners, is: how many sensors are necessary to achieve adequate performance/spatial discrimination? There are many factors to consider in answering this question such as the signal to noise ratio (SNR), the locations and depths of the sources of interest, the density of spatial sampling, sensor gain errors (due to interference, subject movement, cross-talk, etc.) and, of course, the desired spatial discrimination. In this paper, we provide simulations which show the impact these factors have on designing sensor arrays for wearable MEG. While OP-MEG has the potential to provide high information content at dense spatial samplings, we find that adequate spatial discrimination of sources (<1cm) can be achieved with relatively few sensors (<100) at coarse spatial samplings (~30mm) at high SNR. Comparable discrimination for traditional cryogenic systems require far more channels by these same metrics. Finally we show that sensor gain errors have the greatest impact on discrimination between deep sources at high SNR.

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