Biology subjects

Siegel, M.

Publications and source records attributed to Siegel, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Structural and functional MRI signatures of Gambling Disorder: a case-control study

Gambling disorder (GD) is a behavioural addiction that may help identify addiction-related neural features without the direct neurobiological effects of a primary substance of dependence. We examined regional grey matter volume (GMV) and resting-state functional connectivity (rsFC) in the same well-characterised sample. Eighteen men with GD and 21 matched healthy controls underwent high-resolution structural and resting-state functional MRI. GMV was quantified across 214 cortical and subcortical regions, and seed-based rsFC analyses focused on striatal subdivisions and mesocorticolimbic regions. Group differences were evaluated using permutation testing and cluster-corrected mixed-effects modelling. GD was associated with lower GMV in the ventromedial prefrontal cortex, orbitofrontal regions and other cortical and subcortical areas, alongside higher GMV in a subset of limbic and default-mode regions. Participants with GD also showed lower connectivity between the limbic striatum and the hippocampus, thalamus and putamen. In exploratory analyses, somatomotor connectivity was positively associated with gambling severity (Problem Gambling Severity Index: Spearman's rho = 0.71, p = 0.003, false-discovery-rate-adjusted q = 0.016). Structural and functional findings overlapped spatially in regions associated with valuation, memory, reward and habit formation, but regional GMV did not mediate group differences in rsFC. These findings are broadly consistent with corticostriatal models of GD and identify candidate circuit-level differences for independent replication. Larger, more diverse and longitudinal samples are required to establish their reproducibility, temporal direction and clinical relevance.

neuroscience↗

bioRxiv2026

Electrophysiological lag threads reveal a temporal hierarchy of the human cortex

The human brain is spatially organized along functional gradients, but the temporal organization of these gradients remains poorly understood. In EEG and MEG, this question has been difficult to address because volume conduction obscures temporal precedence. Here, we introduce lag-specific orthogonalization, an extension of pairwise orthogonalization that allows us to estimate non-zero-lag amplitude-envelope correlations. Applying this approach to large-scale resting-state MEG, we identify electrophysiological lag threads: reproducible maps of temporal ordering that are most coherent in the alpha and beta bands and organize activity from occipital and sensorimotor regions toward frontal and temporal association cortex. These fast electrophysiological lag threads share subject-specific spatial structure with much slower fMRI lag threads, supporting a neural rather than vascular origin of fMRI lag structure. Finally, we show that cortical lag threads are not fixed anatomical constraints. They covary with age and handedness and are selectively reconfigured during cognitive task performance. Together, these findings reveal a frequency-specific electrophysiological temporal hierarchy of the human cortex that links fast neural dynamics to slower hemodynamic propagation structure.

neuroscience↗

bioRxiv2026

Motor Unit Number Estimation in Magnetomyography

ObjectiveInvestigation of the feasibility and characteristics of contactless motor unit number estimation (MUNE) using optically pumped magnetometer-based magnetomyography (OPM-MMG) as compared to surface electromyography (EMG). MethodsSimultaneous electrically evoked OPM-MMG and EMG signals of the abductor digiti minimi muscle (ADM) were measured in three healthy participants. To characterize MUNE across both modalities and account for within-subject physiological variability, 20 repetitions of electrical stimulation of the ulnar nerve at randomized intensities ranging from 5 to 30 mA in 0.1 mA increments were performed, resulting in a total of 5,020 evoked responses per subject. We quantitatively compared of MUNE and evoked EMG/MMG signal characteristics, including signal-to-noise ratio (SNR) and motor unit response amplitudes. SNR was equalized between measurement modalities to estimate the effect of SNR on MUNE. ResultsMMG-derived MUNE (mMUNE) could be assessed contactlessly. mMUNE estimates were on average 40% lower than EMG-derived MUNE (eMUNE), ranging from 30-65 for mMUNE versus 69-101 for eMUNE. Equalizing the EMG SNR (29-31 dB) to match the MMG SNR (18-27 dB) yielded comparable eMUNE and mMUNE estimates. Peak-to-peak amplitudes of the supramaximal compound motor unit fields ranged from 34-73 pT and single motor unit fields ranged from 0.7-1.4 pT. SignificanceThese findings demonstrate that OPM-MMG enables contactless motor unit number estimation.

neuroscience↗

bioRxiv2026

Extended high frequency hearing influences cortical response amplitudes to speech

Since young adults hear sounds up to 20 kHz, the loss of extended high-frequency hearing (EHF; above 8 kHz) is a hallmark of age-related hearing loss, often progressing from early lifetime. However, this deficit frequently goes undetected because routine clinical hearing tests and most hearing aids are currently limited mostly up to 8 kHz. EHF hearing has been linked to deficits in speech perception in noise and to self-reported hearing. However, it remains elusive how EHF hearing influences speech intelligibility. Here we recorded neuromagnetic brain responses using magnetoencephalography (MEG) within a frequency-tagging speech paradigm designed to probe hierarchical levels of attention and memory-dependent speech processing and recognition. Auditory evoked cortical magnetic field (AEF) responses were significantly reduced in both left and right brain hemispheres in individuals with impaired EHF hearing compared to those subjects with rather preserved EHF hearing. A gradual reinforcement of left-hemispheric AEF seen over age was not observed in young adults (19-29 y) with preserved EHF hearing. This was linked to stronger auditory brainstem responses (ABR), reflecting better neural synchronized auditory responses at stimulus onset. The reinforced left hemispheric dominance in young adults with impaired EHF hearing, in contrast, was linked to lower ABRs. Our findings suggest that sound energy above 8 kHz contributes through its impact on stimulus-onset synchrony to phase locking of oscillations in the auditory cortex to intelligible speech. Together, the results highlight the need to reconsider the neglect of EHF hearing in both audiological assessment and hearing aid design. SignificanceWe show here that deficits in extended high-frequency (EHF) hearing, up to now neglected in routine clinical audiometry and hearing aid technology, lead to reduced cortical evoked auditory field (AEF) response amplitudes to attended and unattended speech, even at a young age. A gradual increase in reinforced left-hemispheric AEF responses during attended speech does not occur in young people with good EHF hearing; this is linked to better synchronization of neural responses at the onset of sound. This suggests a crucial role of sounds containing energy above 8kHz in minimizing the need for cognitive resources during active listening. Collectively, our results challenge current clinical practices and underscore the need to incorporate EHF hearing into audiological assessment and hearing aid design.

neuroscience↗

bioRxiv2026

From Coarse to Rich: Successive Waves of Visual Perception in Prefrontal Cortex

The ventrolateral prefrontal cortex (vlPFC) is well known for its involvement in high-level functions such as cognitive control and language. However, vlPFCs role in visual processing is less clear. Here, we investigated how neuronal ensembles in the vlPFC dynamically encode different types of visual information. Using chronic recording of spiking activity, we investigated vlPFCs representational geometry in a macaque monkey passively viewing a large set of naturalistic images, and compared this to representations in deep neural networks (DNNs). We found that the vlPFC processes visual information in two stages. First, an "early" response from 50 to 90 ms after stimulus onset encodes the low spatial frequency component of an image. It contains sufficient information to form a coarse estimate of the position and category of a salient object. Then, from 100 ms on, the representational geometry changes and contains much richer information. This late period contains non-categorical information typically present in conscious experiences such as the orientation of a face and natural scenes in the background. The late window also enables sub-category identification, which is boosted by the low spatial category prior. These results suggest that the vlPFC has a dual role in natural vision: first forming fast low-spatial-frequency-based priors shaping feed-forward visual processing, and subsequently maintaining a detailed and rich representation of a visual scene. SignificanceWhat information does the prefrontal cortex represent during natural vision, and how does this relate to conscious experience? Theories of consciousness differ sharply on whether prefrontal activity reflects detailed perceptual content or only high-level, task-related information. Using dense neural recordings during passive viewing of thousands of naturalistic images, we show that the ventrolateral prefrontal cortex processes visual information in two distinct stages: an early, coarse estimate of the visual scene is followed by a richer, high-dimensional representation that includes sub-category identity and other perceptual details. These findings reveal that prefrontal circuits encode more of the content of visual experience than previously assumed, even in the absence of a task.

neuroscience↗

bioRxiv2026

Lempel-Ziv complexity of simultaneous surface electromyography and magnetomyography during muscle fatigue

ObjectiveComplexity-based metrics have been applied to surface electromyography (sEMG) to characterize fatigue-related changes in the temporal structure of myoelectric signals beyond amplitude and spectral features. Optically pumped magnetometers (OPM) are sensors that enable non-invasive recordings of magnetomyographic (MMG) signals from skeletal muscle and are increasingly used to complement surface electromyography; however, it remains unclear whether complexity measures derived from magnetic recordings are comparable to those obtained from sEMG. Here, we directly compared fatigue-related dynamics of conventional and complexity-based signal features of sEMG and OPM-MMG measured from the biceps brachii during sustained elbow flexion. ApproachHealthy participants performed isometric contractions at 20% maximal voluntary contraction (MVC; 20 min) or 60% MVC (3 min). sEMG and OPM-based MMG were recorded simultaneously, and signal median frequency, root mean square (RMS), and Lempel-Ziv (LZ) complexity were calculated over time. Main resultsAcross contraction intensities, sEMG and MMG showed consistent fatigue-related changes, characterized by increasing RMS, decreasing median frequency, and a progressive decline in LZ over time. In addition, multiple regression analyses indicated that the decrease in LZ was not fully accounted for by concurrent amplitude or spectral changes, suggesting that complexity captures aspects of signal organization that are not fully explained by established features. Finally, while sEMG showed higher LZ complexity and median frequency at 60% compared to 20% MVC, corresponding intensity-dependent effects were not observed in OPM-based MMG. SignificanceThese findings suggest that complexity-based metrics capture fatigue-related changes in neuromuscular signal organization beyond conventional measures, and that sEMG and OPM-based MMG provide similar, though modality-specific, information. Together, the results support the use of complexity metrics in multimodal electrophysiological and biomagnetic assessments of neuromuscular fatigue.

neuroscience↗

bioRxiv2025

Rapid fluorescence lifetime sensor development of LifeCamp enables transient and baseline absolute calcium measurements

Genetically encoded calcium sensors (GECIs) have been instrumental for studying neuronal activity and intracellular signaling. GECIs are typically fluorescence-intensity sensors that change brightness upon calcium binding. Iterative improvements in GECIs have yielded indicators that report action potential-evoked calcium entry with high sensitivity and temporal resolution, enabling measurement of network activity across thousands of neurons. However, fluorescence intensity-based measurements generally cannot report baseline or absolute calcium levels and may confound neuromodulatory regulation of calcium handling with changes in action potential firing. Fluorescence lifetime sensors are insensitive to many artifacts that plague intensity-based measures and report absolute substrate levels, including those at rest. However, relatively few lifetime sensors for neuronal signals exist, and developing new sensors is typically difficult and low-yield. Here, we introduce a new rapid lifetime sensor development (RALISED) platform, which we use to build a new GCaMP8m-based high-speed lifetime calcium sensor, termed LifeCamp. We show that LifeCamp enables comparison of baseline calcium signals in cell culture, brain slices, and mice. In addition, we show that LifeCamp enables the detection of fast action potential-evoked calcium transients in single neurons from brain slices and in behaving mice. Using LifeCamp, we discovered calcium baseline changes associated with neuronal activity in brain slices and behaving mice, as well as slow average calcium changes in neuronal populations of freely moving mice. Altogether, this study highlights the utility of the RALISED method to rapidly develop new lifetime sensors and the application of the LifeCamp calcium lifetime sensor to study fast and slow calcium signaling. Significance statementWe developed a new high-speed, sensitive calcium lifetime sensor (LifeCamp) using a novel rapid lifetime sensor development (RALISED) platform. LifeCamp has high sensitivity and a large substrate-dependent lifetime change (<1ns), allowing for the capture of baseline calcium levels, transient calcium changes, and neuronal firing in vitro and behaving animals. LifeCamp lifetime measurement is insensitive to artifacts that plague conventional intensity imaging and enables absolute comparison of baseline and transient calcium changes across cells, brain regions, and experimental conditions. Hence, LifeCamp is a powerful tool that enables a more accurate and in-depth understanding of neuronal activity and calcium signaling.

neuroscience↗

bioRxiv2025

Portable quantum-sensor magnetomyography decodes fine hand movements

Optically pumped magnetometers (OPMs) are compact quantum sensors that can measure the magnetic fields generated by muscle activity (magnetomyography, MMG) without skin contact. This contact-free alternative to surface electromyography (EMG) has remained mostly confined to magnetically shielded rooms and simple tasks, limiting translation. Here, we show that a portable OPM-MMG system operated inside a compact magnetic shield can robustly decode fine finger movements and recover EMG-like muscle activation patterns. Eight participants executed flexion-extension combinations spanning 15 finger actions while we recorded triaxial MMG and bipolar EMG concurrently. MMG supported robust multi-class and finger-specific decoding, recovering a representational geometry that closely matched EMG. Orientation analyses showed that components orthogonal to the muscle axis contributed most significantly to discriminability, providing actionable guidance for OPM array design. Our results show that OPM-MMG in a portable shielded environment preserves task-relevant neuromuscular information and approaches EMG-level structure without skin contact or a magnetically shielded room. Our findings open a path toward hygienic, rapid-setup assessment and human-machine interfacing in clinics and rehabilitation settings.

neuroscience↗

bioRxiv2025

Visual features underlying neural selectivity for natural stimuli

Neural activity is selective for the identity and semantic category of natural visual stimuli. In human neuroimaging, this selectivity can be investigated using multivariate pattern analysis. The resulting neural information is thought to reflect neural processes underlying object recognition. However, due to the complexity of natural stimuli, it remains unclear to what extent different visual features drive this information. To address this, we presented natural stimuli from different categories as well as several degraded versions, in which specific visual features were selectively manipulated, to human participants while recording magnetoencephalography (MEG). We applied multivariate pattern analysis and decomposed the resulting high-dimensional neural representational geometry into its distinct contributions from retinotopy, total spatial frequency and orientation energy, global shape, local shape, and textural correlation structure. These features contributed differentially to the neural information, with retinotopy and global shape being the strongest features underlying image and category information, respectively. Our results demonstrate how multiple statistical features of visual stimuli jointly and dynamically account for neural image and category selectivity, providing a basis for a better understanding of human object recognition.

neuroscience↗

bioRxiv2025

An artifact-robust framework for measuring tCS effects during stimulation

ObjectiveTranscranial current stimulation (tCS) is a promising technique to non-invasively modulate human brain activity. However, stimulation artifacts in EEG and MEG recordings severely hinder the study of its online effects. ApproachHere, we introduce a new approach to account for these artifacts. The approach rests on two key ideas. First, we focus on interactions of tCS with intrinsic brain activity, which are absent for tCS artifacts. Second, rather than removing artifacts, we compare composite signals that share similar artifacts but potentially differ in the interaction of tCS with intrinsic brain activity. We follow a simple logic: if tCS does not interact with intrinsic brain activity, then neural activity during simultaneous sensory stimulation and tCS should equal the linear superposition of the neural effects of each applied alone. Any deviation from this prediction provides evidence for a neural interaction. Main resultsWe tested this approach in a proof-of-principle MEG study, applying 10 Hz transcranial alternating current stimulation (tACS) during rest and during a 10 Hz visual flicker. We compared neural activity during simultaneous stimulation with that predicted by the linear superposition of flicker and tACS alone and found a phase-dependent interaction between tACS and flicker-evoked brain activity. SignificanceOur work establishes a novel approach to investigate online effects of tCS and suggests a state-dependent interaction of tACS with human brain activity.

neuroscience↗

bioRxiv2025

Morphologically Tunable Mycelium Chips for Physical Reservoir Computing

AO_SCPLOWBSTRACTC_SCPLOWWe introduce a neuromorphic computing substrate based on PEDOT:PSS-infused mycelium, a biofabricated, morphologically tunable material that can be engineered into electrically active components including resistors, capacitors, and non-linear elements. Leveraging the principles of physical reservoir computing, we demonstrate that hyphal networks grown under controlled environmental conditions can transform time-varying inputs into nonlinear, high-dimensional state trajectories, enabling machine learning tasks such as NARMA-10 sequence prediction. The chips are produced using a "design-grow-compute" workflow that integrates morphological modeling, parametric growth protocols, and vacuum-assisted polymer infusion. Morphological complexity is shown to influence charge transport and memory capacity, offering a new axis of control for designing analog computational architectures. Our prototype chips interface with a custom carrier board enabling analog signal conditioning and readout. Benchmarking revealed robust nonlinearity, temporal dynamics, and task-relevant separability. Unlike memristor arrays, photonic, or living-cell-based reservoir systems, our non-living analog mycelium chip is low-cost, biodegradable, and scalable using existing mushroom farming infrastructure, with production yields exceeding 3 million chips per growth cycle. This proof-of-concept demonstration using a single prototype device establishes the first biodegradable reservoir computing platform, with performance trade-offs justified by unprecedented sustainability advantages and orders-of-magnitude cost reductions. This advances a novel direction for biologically derived, single-use (compostable) or very large-scale machine learning hardware and introduces mycelium as a functional medium for analog inference.

bioengineering↗

bioRxiv2025

Robust spatial hearing beyond primary interaural cues in humans over deep neural networks

Spatial hearing allows humans to localize sound sources in the azimuth plane using interaural time (ITD) and level (ILD) differences, but the contribution of additional auditory features remains unclear. To investigate this, we measured human localization performance with natural and artificial stimuli that selectively included or excluded ITD and ILD as primary interaural cues. As expected, human listeners relied synergistically on ITD and ILD for accurate azimuth localization. Moreover, even when both primary cues were absent, localization performance remained above chance level. We compared human performance with state-of-the-art deep neural networks (DNN) optimized for sound localization to investigate possible computational mechanisms underlying this robust performance. In contrast to humans, DNNs demonstrated high accuracy only for stimuli that resembled their training regime but failed when primary interaural cues were absent. This human-DNN misalignment highlights a fundamental distinction in sensory processing strategies, potentially arising from the simplicity bias inherent in DNN training, with human reliance on a wider range of auditory features likely reflecting evolutionary pressures favoring adaptability across diverse acoustic environments. Together, our results demonstrate the robustness of human spatial hearing beyond primary interaural cues and point to promising directions for advancing artificial systems and informing clinical applications, such as cochlear implants and auditory prosthetics.

animal behavior and cognition↗

bioRxiv2025

Frontocentral brain networks amplify action-oriented temporal prediction errors

Successful sensorimotor behavior depends on anticipating when a tactile event will occur and translating it into rapid action, yet it remains unclear how action requirements shape temporal prediction in the human cortex. Here, we combined Magnetoencephalography (MEG), multivariate decoding and computational modeling to contrast temporally jittered finger stimulations under passive versus action-oriented conditions. We characterized a distributed frontal sensorimotor network that was linked to fast tactile-motor associations and predicted reaction-time variability. We found that trial-by-trial deviations from expected stimulus timing modulated behavior, with stronger frontocentral prediction-error encoding when an overt response was required. Temporal prediction errors were primarily encoded in a motor rather than sensory reference frame. These results reveal that action demands dynamically amplify temporal error signaling in sensorimotor circuits, in line with predictive coding frameworks postulating that the brain preferentially encodes task-relevant and goal-directed sensory prediction errors.

neuroscience↗

bioRxiv2025

Human Lung Alveolar Model with an Autologous Innate and Adaptive Immune Compartment

Lung-resident immune cells, spanning both innate and adaptive compartments, preserve the integrity of the respiratory barrier, but become pathogenic if dysregulated1. Current in vitro organoid models aim to replicate interactions between the alveolar epithelium and immune cells but have not yet incorporated lung-specific immune cells critical for tissue residency2. Here we address this shortcoming by describing human lung alveolar immuno-organoids (LIO) that contain an autologous tissue-resident lymphoid compartment, primarily composed of tissue-resident memory T cells (TRMs). Additionally, we introduce lung alveolar immuno-organoids with myeloid cells (LIOM), which include both TRMs and a macrophage-rich alveolar myeloid compartment. The resident immune cells formed a stable immune-epithelial system, frequently interacting with the epithelium and promoting a regenerative alveolar transcriptomic profile. To understand how dysregulated inflammation perturbed the respiratory barrier, we simulated T-cell-mediated inflammation in LIOs and LIOMs and used single-cell transcriptomic analyses to uncover the molecular mechanisms driving immune responses. The presence of innate cells induced a shift in T cell identity from cytotoxic to immunosuppressive, reducing epithelial cell killing and inflammation. Based on insights obtained with bulk RNA-seq data from the phase 3 IMpower150 trial, we tested whether LIOM cultures could model clinically-relevant but poorly understood pulmonary side effects caused by immunotherapies such as the checkpoint inhibitor atezolizumab3. We observed a decrease in immunosuppressive T cells and identified gene signatures that matched the transcriptomic profile of patients with drug-induced pneumonitis. Given its effectiveness in capturing outcomes and mechanisms associated with a prevalent pulmonary disease, this system unlocks opportunities for studying a wide range of immune-related pathologies in the lung.

cell biology↗

bioRxiv2025

Absolute measurement of fast and slow neuronal signals with fluorescence lifetime photometry at high temporal resolution

The concentrations of extracellular and intracellular signaling molecules, such as dopamine and cAMP, change over both fast and slow timescales and impact downstream pathways in a cell-type specific manner. Fluorescence sensors currently used to monitor such signals in vivo are typically optimized to detect fast, relative changes in concentration of the target molecule. They are less well suited to detect slowly-changing signals and rarely provide absolute measurements of either fast and slow signaling components. Here, we developed a system for fluorescence lifetime photometry at high temporal resolution (FLIPR) that utilizes frequency-domain analog processing to measure the absolute fluorescence lifetime of genetically-encoded sensors at high speed but with long-term stability and picosecond precision in freely moving mice. We applied FLIPR to investigate dopamine signaling in two functionally distinct regions in the striatum, the nucleus accumbens core (NAC) and the tail of striatum (TOS). We observed higher tonic dopamine levels at baseline in the TOS compared to the NAC and detected differential and dynamic responses in phasic and tonic dopamine to appetitive and aversive stimuli. Thus, FLIPR enables simple monitoring of fast and slow time-scale neuronal signaling in absolute units, revealing previously unappreciated spatial and temporal variation even in well-studied signaling systems.

neuroscience↗

bioRxiv2025

Quantifying Differences in Neural Population Activity With Shape Metrics

Quantifying differences across species and individuals is fundamental to many fields of biology. However, it remains challenging to draw detailed functional comparisons between large populations of interacting neurons. Here, we introduce a general framework for comparing neural population activity in terms of shape distances. This approach defines similarity in terms of explicit geometric transformations, which can be flexibly specified to obtain different measures of population-level neural similarity. Moreover, differences between systems are defined by a distance that is symmetric and satisfies the triangle inequality, enabling downstream analyses such as clustering and nearest-neighbor regression. We demonstrate this approach on datasets spanning multiple behavioral tasks (navigation, passive viewing of images, and decision making) and species (mice and non-human primates), highlighting its potential to measure functional variability across subjects and brain regions, as well as its ability to relate neural geometry to animal behavior.

neuroscience↗

bioRxiv2024

Abstract choice representations during stable choice-response associations

Perceptual decisions have long been framed in terms of the actions used to report a choice. Accordingly, studies of perceptual decision-making have historically relied on tasks with fixed choice-response mappings, in which choice and motor response are inextricably linked. Although several studies have since dissociated choice and response, they have typically involved dynamic switching of the choice-response mapping on a trial-by-trial basis. Thus, it remains unclear if abstract choice representations arise specifically when choice-response relationships change dynamically, or if they reflect a more general property of the decision-making process. Here, we show that in the human brain, choices are represented abstractly, even when the association between choice and motor response remains stable over time. We measured neural activity in humans using magnetoencephalography (MEG) while participants performed a motion discrimination task. Importantly, the associations between perceptual choice and motor response were balanced across experimental conditions and remained stable over many trials. We found neural information about the participants perceptual choice, independent of both the motor response and the visual stimulus. This abstract choice information increased during the stimulus period and peaked after the response had been made. Furthermore, choice and response information showed distinct cortical distributions, with strongest choice information in frontoparietal regions. Our results suggest that abstract choice representations are not restricted to action-independent contexts or those with dynamic choice-response associations and may therefore reflect a general role in perceptual decision-making.

neuroscience↗

bioRxiv2024

Detecting single motor-unit activity in magnetomyography

Studying the discharge patterns of motor units (MUs) is key to understanding the mechanisms underlying human motor behavior. Intramuscular electromyography (iEMG) allows direct study of MU activity, but is invasive. Surface electromyography (sEMG) offers a non-invasive alternative, but with lower spatial resolution. Recent advances in optically pumped magnetometers (OPMs) have sparked interest in the magnetic counterpart of EMG, magnetomyography (MMG), as an additional non-contact modality to study the neuromuscular system. However, it remains unclear whether MMG signals recorded with superconducting quantum interference devices (SQUIDs) or OPMs can be used to directly detect individual MUs. We addressed this question in a proof-of-principle study in which we recorded MMG signals from the abductor digiti minimi (ADM) muscle using SQUIDs and OPMs. Critically, we simultaneously recorded iEMG from the same muscle to validate the non-invasive measurements. First, we found that invasively recorded MUs can be detected in simultaneously recorded SQUID and OPM MMG signals. Second, we found that invasively validated MUs can be extracted directly from SQUID and OPM MMG. This provides converging evidence that individual MU activity is accessible using non-contact MMG. Our findings highlight the potential of MMG as a non-contact modality to measure and study muscle activity in health and disease.

neuroscience↗