NeuroImage

Understanding the neural basis of human cognition requires measuring not only localized brain activity but also how functional interactions between brain regions change in response to different cognitive demands. Event-related fMRI is an efficient design for linking trial-wise behavioral and computational variables to brain activity, but comparable methods for examining their effects on functional connectivity remain limited. Here, we develop beta-PPI (beta-series psychophysiological interaction), a method that leverages single-trial response estimates from even-related fMRI to quantify how trial-wise variables modulate functional connectivity. This task-based functional connectivity method provides a flexible approach for studying functional connectivity for event-related fMRI designs. We evaluated beta-PPI using comprehensive simulations across several experimental conditions and signal qualities. Beta-PPI can sensitively detect ground-truth effects and exhibited good parameter recovery. Compared with generalized psychophysiological interaction, beta-PPI achieved comparable performance across most conditions while demonstrating improved statistical power under lower signal-to-noise conditions. We further validated beta-PPI using empirical event-related fMRI data. Distinct trial-wise cognitive variables selectively modulated functional connectivity during their corresponding trial epochs, demonstrating the temporal specificity and flexibility of the approach. By testing how trial-wise variables modulate functional connectivity, beta-PPI extends task-based connectivity analysis to model-based fMRI and provides a common single-trial framework that could facilitate the integration of connectivity, activation, and representational analyses in event-related fMRI.

Diffusion MRI (dMRI) enables noninvasive characterization of white-matter fiber orientations and tissue microstructure, but widely used approaches, such as constrained spherical deconvolution (CSD) and parametric multicompartment models, typically address these features separately. The diffusion tensor distribution (DTD) framework jointly represents fiber orientation and microstructure, but estimating DTD from finite, noisy measurements is severely ill-posed. Existing inversion methods either rely on nonnegativity constrained basis representations, which are challenging to sale to high-dimensional and high-resolution distributions, or use sampling-based approaches with limited reliability. We propose MaxEnt-DTD, a maximum-entropy algorithm for DTD estimation from finite and noisy dMRI data. By deriving the Lagrange dual formulation, we reformulate a constrained infinite-dimensional optimization problem into a finite-dimensional unconstrained convex optimization problem, substantially reducing the parameter space and enabling tractable whole-brain DTD estimation. We evaluate MaxEnt-DTD using both synthetic and in vivo data from the Human Connectome Project protocol and a second dataset using advanced B-tensor diffusion encoding. We compare MaxEnt-DTD-derived fiber orientation distributions with results from CSD and Monte-Carlo inversion methods, and assess fiber-specific microstructure measures and rotation-invariant metrics based on the cumulants of DTD. The results demonstrate that MaxEnt-DTD provides a reliable and efficient framework for joint fiber-orientation and microstructure analysis in dMRI.

State-transition approaches, including EEG microstate analysis and related fMRI methods such as hidden Markov models (HMMs) and co-activation pattern (CAP) analysis, provide widely used tools for coarse-graining neural dynamics into a small set of quasi-stable states. Its utility has been demonstrated across resting-state and task paradigms, with broad applications ranging from cognitive neuroscience to candidate biomarkers for psychiatric and neurological disorders. A fundamental limitation remains, however: nearly all downstream temporal measures are conditional on the template maps defined at the outset. In the conventional pipeline, templates are derived from polarity-invariant clustering of voltage maps at global field power (GFP) peaks, making the resulting state definitions sensitive to preprocessing, sampling, initialization, clustering algorithms, and the choice of cluster number. Consequently, the method captures coarse regularities in EEG dynamics, while only weakly constraining the larger geometric organization from which those states emerge. This template dependence poses a major challenge for reproducibility and for comparisons across studies and EEG caps. Here, we revisit this problem from a topological-geometric perspective. We treat templates not as cluster centroids extracted from GFP-peak maps, but as landmarks embedded in the global structure of a state space constructed from mutual similarities among scalp voltage maps. In this formulation, microstate templates are rediscovered as discrete representatives of dominant axes that organize continuous neural-state topography. This reformulation preserves polarity as a meaningful geometric relation instead of eliminating it at the outset as analytical redundancy. It also shifts attention from isolated state labels to the terrain of the state space itself: the broader relational structure within which local states become interpretable. Using this approach, we show that landmark-based state definitions outperform conventional templates in capturing state structure and improving analytical performance. These findings suggest that the central problem in EEG microstate analysis is broader than clustering optimization: it concerns how to define valid nodes for coarse-graining continuous dynamics without discarding the topology that organizes them. By shifting the conceptual basis of microstate analysis from templates to landmarks, the present approach provides a more principled and potentially more stable foundation for state definition, including in fMRI. This topolo-geometric reappraisal extends conventional microstate analysis and opens a path toward more unified comparisons across datasets, paradigms, and recording systems.

Subanaesthetic ketamine alters the content of consciousness while leaving responsiveness intact. We asked whether this state can be decoded from single eyes-closed EEG epochs, and how spectral power and phase-based connectivity compare when used as features. Re-analysing openly available 62-channel EEG from ten participants (awake versus subanaesthetic ketamine), we trained classifiers under leave-one-subject-out cross-validation to discriminate the two conditions from log band power, weighted phase-lag index (wPLI) connectivity, and their concatenation. Band power decoded the ketamine state above chance (balanced accuracy 0.71), whereas wPLI connectivity computed on the same epochs was at chance (0.47), and combining the feature sets did not improve on power alone. The dissociation held across three classifier families and across spatial montages, and was not explained by the dimensionality of the connectivity feature space. To identify its source, we decomposed the per-feature drug effect into components shared across subjects and subject-specific. The ketamine effect on connectivity was large within individuals but largely subject-specific, and therefore not transferable to held-out subjects (shared fraction 0.05), whereas the spectral effect was substantially shared across subjects (0.53); both feature classes carried comparable individual identity, so the asymmetry reflects transferability rather than fingerprint-likeness. The spectral signature was also recoverable from a sparse electrode subset, as a five-channel lateral montage performed as well as the full array. Undirected phase connectivity thus fails to decode subanaesthetic ketamine not through insufficient spatial sampling, but because the connectivity drug effect is subject-specific in form.

Structural covariance networks (SCNs) represent spatial patterns of covariation in brain morphology, often as a network of connections between nodes representing correlations in grey matter volume or cor- tical thickness measured by magnetic resonance imaging (MRI). SCNs have been suggested to reveal differences in functional organisation that are reflected in coordinated alterations to brain structure, and often these differences are sought in graph-theoretic measures such as the degree of clustering, segregation into distinct modules, or the characteristic path length between nodes. A common practice is to calculate SCNs for groups of interest, and use permutation testing to determine if they are significantly different for the measure of interest. However, the statistical validity of group comparisons using SCN-derived graph measures remains poorly understood. Here, we systematically evaluate the reliability of SCN estimation and downstream graph-theoretic anal- yses using structural MRI data from the Human Connectome Project ( = 1,096). We use simulations to show the effects of sample size and atlas dimensionality on SCN reliability. Using bootstrapping to characterise the distribution of SCN graph measures, we establish that small sample sizes systematically bias graph-theoretic measures including clustering, characteristic path length and modularity. Finally, we use simulations based on extrema from the bootstrapping distribution to characterise the statistical power and false discovery rate (FDR) for graph-theoretic between-group comparisons of SCNs, showing that at small sample sizes ( [≤] 30) permutation testing is no better than chance. These findings suggest that many significant SCN group differences, particularly those using small sam- ples and high-dimensional parcellations, may reflect sampling noise rather than true biological differences. We recommend that future SCN studies use larger samples, coarser parcellations, and explicitly evaluate reliability before interpreting group differences.

Speech cortical mapping by means of navigated repetitive transcranial magnetic stimulation (SCM nrTMS) provides neurosurgeons with noninvasive prior information about individuals cortical speech network. Individualized mapping is required, since the exact locations and activation patterns of speech production show high variability between individuals. We hypothesized that magnetoencephalography (MEG) data of an individuals speech production could guide the SCM TMS process temporally and spatially, leading to higher error rates at MEG-defined locations with TMS pulse timings coinciding with MEG activity. 13 healthy subjects participated in MEG and TMS measurements, where the timing of the TMS pulse (PTI; picture-to-TMS interval) was adjusted based on the individuals MEG activation in a picture naming task. At the group level, significant correlations were observed between the latency of the peak MEG activation and the PTI that produced the highest speech error rate. The MEG peak preceded the best PTI by 132 ms (R=0.713, p=0.006) across the entire stimulation area in the lateral left hemisphere, and by 103 ms (R=0.673, p=0.012) in the left frontal regions. We found 17 combinations of PTI and stimulation area in which the subjects speech error rate increased significantly compared to their average error rate. Our findings suggest that optimal PTIs are highly individual, and that individualizing the PTI according to MEG activation provides a straightforward method for accounting individual variability in speech function and may increase the sensitivity and utility of SCM TMS.

The thalamic reticular nucleus (TRN) is a thin, inhibitory shell surrounding the thalamus. It regulates the thalamocortical information flow, and thereby plays a central role in attention, task switching, and the sleep-wake cycle. Despite its importance, the TRN remains poorly studied in the human brain. This is largely because its small size and deep anatomical location limit its visibility with conventional non-invasive neuroimaging techniques. Here, we assessed whether the human TRN can be reliably visualised and segmented in vivo using ultra-high field (UHF) magnetic resonance imaging (MRI) at 7 Tesla. High resolution (0.35 mm isotropic) partial-brain T2* and T1 scans were acquired from healthy individuals, followed by manual delineation of the TRN. These in vivo segmentations were compared with TRN estimates obtained from two high-quality postmortem datasets serving as an anatomical reference. In vivo segmentations of TRN volume and thickness closely matched measurements derived from the postmortem reference datasets, and quantitative comparisons showed high consistency in TRN shape and location across individuals while also capturing meaningful inter-individual variability. Using these segmentations, we constructed a publicly available probabilistic atlas of the human TRN. This atlas provides a new resource for incorporating TRN anatomy into functional, structural, and clinical neuroimaging studies. Our findings demonstrate that the human TRN can be robustly mapped in vivo at 7T and establish a foundation for future investigations into its structure and function.

The heartbeat-evoked potential (HEP), a cortical response to heartbeats and a neural marker of interoception, is increasingly considered clinically relevant, but is heavily contaminated by cardiac artefact (CA) on the scalp, making reliable distinction of HEP from CA challenging. Because the HEP and cardiac potentials are anatomically distinct, they may be separable via beamforming, a source localisation method that isolates brain activity at specific locations while suppressing external noise and interference. Here, the first known ground-truth validation of EEG beamforming for HEP source reconstruction was attempted, aiming to quantify source waveform recovery and spatial localisation accuracy using simulated EEG data. Using linearly constrained minimal variance (LCMV) beamforming, the following was investigated. (A) To test whether beamforming can recover a known signal, 128-channel EEG datasets were simulated for 3 models with a known HEP waveform: a single right insula (R-Ins) HEP (1), two temporally distinct HEPs in the R-Ins and right anterior cingulate cortex (R-ACC) (2), and two temporally overlapping HEPs in the same regions (3). Recovery was investigated by correlating the virtual electrode waveforms at the true location with the known true input waveform. (B) To test CA suppression, CA extracted from isoelectric EEG of brain-dead individuals providing CA with limited cortical activity, was integrated into the simulated EEG data. Source (-10 to -50dB) and sensor (0 to -30dB) signal-to-noise ratios (SNR) were systematically varied for each model with and without CA. (C) LCMV beamforming was then applied to retrospective empirical EEG data from hypertensive and anxiety individuals (n=106). Across simulations and empirical data, T-tests compared power in a HEP-dominated window to an earlier CA-dominated window. Null-space projection, using subject-specific QRS waveform and its temporal derivative, was applied to remove residual CA in reconstructed source waveforms. In model 1, beamforming achieved near-perfect recovery without CA (r>0.99, 0mm error) at source SNR of -30dB, remaining robust in the presence of CA (r=0.72-0.94, 0-5.7mm error). Recovery degraded at low SNR (<-20 dB; r<0.3, up to 27mm error). Models 2 and 3 showed similar patterns but introduced R-ACC to R-Ins leakage. Applied to empirical data, beamforming revealed significant HEP activity in the R-Ins and R-ACC across all pooled data in source space (p < 0.001). LCMV beamforming also revealed a significant HEP difference in the late R-ACC window (250-500 ms post R-peak) in hypertension versus controls (p = 0.040, d = 0.92) when poor SNR subjects were excluded. This effect strengthened after QRS cleaning (p = 0.035, d = 0.96). LCMV beamforming can reliably recover the simulated HEP while suppressing CA, provided source SNR is sufficiently high. Applied to empirical data, LCMV beamforming recovered HEP activity from interoceptive regions (R-Ins and R-ACC) and was sufficiently sensitive to detect clinically meaningful group differences. This study offers a source level approach to separate HEP activity from CA, a distinction that sensor-level analysis can struggle to make. Together, LCMV beamforming and scalp-based methods can provide converging evidence for genuine HEP activity.

Substantial progress has been made in recent years on understanding how the human brain parses and processes natural speech. Much of this progress has been based on modeling how brain activity relates to the different acoustic and linguistic features of speech. By fitting and testing models based on those features, one can test hypotheses about the kinds of computations and representations the brain uses to convert speech sounds into understanding. While much of this work has focused on modeling BOLD activity using functional neuroimaging or intracranially recorded electrophysiological signals, the approach has also proven useful with MEG and EEG. Indeed, noninvasive EEG has certain advantages for studying speech processing in terms of translational research and application. Research over the last decade or so has shown that EEG can be successfully modeled based on numerous acoustic, linguistic, and paralinguistic speech features. However, an important unanswered question hangs over all of this work: namely, what constitutes a good model of EEG responses to natural speech? Or, to put it another way, how much variance in EEG recorded during natural speech listening is explainable as having derived from that speech input? The present study aims to tackle this issue. We do so under the assumption that the best model for a person's EEG response to natural speech is a set of EEG responses from other people listening to the same speech. Using this assumption, we construct inter-subject models using EEG from 19 healthy adult native speakers of English who all listened to the same audiobook. The model for each subject involves predicting their EEG data using (dimensionality-reduced) EEG from different numbers of other subjects and then extrapolating to estimate the total explainable variance in the target individual's response to speech. Following this, we show that linear models (temporal response functions) based on several commonly used acoustic and linguistic speech features can predict most - but importantly not all - of the estimated total explainable variance in EEG responses across subjects.

Multicenter neuroimaging studies face challenges from scanner-related measurement biases that can obscure biological effects. Traveling-subject (TS) designs allow direct quantification of such biases by scanning the same participants across multiple sites. While reliability of structural, functional, and diffusion MRI has been established, the reliability of electric field simulations for transcranial brain stimulation has not been examined. We assessed inter-scanner and scan-rescan reliability of simulated electric field magnitudes in 10 participants scanned twice on each of five 3T Siemens scanners, using SimNIBS v4.1 to simulate focal tDCS across seven cortical and cerebellar targets. ICC values indicated good to excellent inter-scanner (0.86-0.97) and scan-rescan (0.94-0.98) reliability, with between-scanner variance not exceeding measurement error. Intra-individual segmentation variability substantially explained within-person differences in field magnitudes, whereas image quality did not predict segmentation variability. These results demonstrate that individualized electric field simulations are robust to scanner-related variation, supporting their use in multicenter brain stimulation research.

Vector autoregressive (VAR) models have a history of being used to examine functional connectivity in the brain, as captured by functional MRI studies. Such models allow for an estimation of Granger-causal relationships between regions of interest across the brain. Unfortunately, since the number of parameters in the VAR model scales as the square of the number of regions, and this is typically large compared to the number of temporal observations, these parameter estimates will exhibit high variance. To address this challenge, we introduce a low-rank pre-smoothing method that applies a low-rank approximation to the observations before fitting a VAR model. We estimate these models using individual subject data from both task-based and resting-state conditions, tuning hyperparameters at the population level. Our low-rank approach is directly compared against sparse and unconstrained estimation methods. Evaluations of predictive performance and model structure reveal that our pre-smoothing technique enables robust individual-level parameter estimation and significantly reduces prediction error, a finding further validated by synthetic experiments where the ground-truth parameters are known.

Functional MRI provides a powerful way to characterize the architecture of the human brain. Yet individual brains often vary in the exact anatomic locations of functionally specific regions; as a result of this variability, traditional random-effects group analyses (RFX) often require unrealistically large sample sizes to converge onto stable results. Here, we explore the scaling patterns for an alternative fMRI analysis framework that accounts for inter-individual topographic variability while still capturing spatial similarities--group-constrained, subject-specific (GcSS) analysis. Using sixteen contrasts from four Human Connectome Project tasks (spanning language, social cognition, motor, and working memory), we first show that the GcSS approach yields much larger effect sizes than traditional RFX analyses. We then investigate the impact of sample size (N=10-450) on the estimation of three key GcSS outputs: (1) group probabilistic maps for the contrast of interest, (2) group-level parcels that serve as anatomical constraints for subsequent functional region of interest (fROI) definitions in individual subjects, and (3) effect sizes of fROI responses to task conditions. For most contrasts, the probabilistic maps show good reliability at N=100 (intraclass correlation, ICC=.75) and excellent reliability at N=200 (ICC=.90). Most group parcels also achieve substantial agreement at N=100 (Dice coefficient, DC=.80). Critically, once robust group parcels are established, the subject-specific portion of the analysis can proceed with much smaller sample sizes: even samples of N=10 participants yield accurate effect size estimates within subject-specific fROIs, detecting 80% of practically meaningful effects (effect size [&ge;] 0.2); with N=20, this increases to 90%. The scaling patterns we observed held not only in the cortex, but also in the cerebellum. Our results challenge the view that fMRI research requires large samples: once the broad region of interest is established (with N=100 or higher), fROI-based analyses can yield generalizable results with small sample sizes (N=10 or N=20).

The prenatal period of human brain development is critical for mental health and cognition across the entire lifespan. During this period, the cortex undergoes a dramatic transformation from a smooth lissencephalic surface into an elaborately folded structure, a process whose precise characterization is essential for understanding neurodevelopmental trajectories. This study represents the first application of Spectral Analysis of Gyrification (SPANGY) to a large multi-centric fetal brain MRI dataset (635 subjects, 20-38 weeks gestational age). SPANGY characterizes geometric variations on a surface based on the wavelength of folds, hence, providing a quantitative local description of gyrification at the individual level. Using rigorous normative modeling (GAMLSS) and statistical harmonization (ComBat-GAM), we established age-specific reference trajectories for multi-scale gyrification features (spectral frequency bands). We provide the first ever quantification of the temporally-ordered emergence of cortical folding in successive waves: the earliest-emerging low frequency, deep fissures are progressively superseded by the accelerating expansion of higher frequency folds. The normative curves provide the first step in taking prenatal neurodevelopmental assessment from qualitative inspection into a rigorous statistical inference, creating an objective reference against which deviations from healthy brain growth can be caught earlier, and with greater precision.

Introduction: Structural neuroimaging relies on T1-weighted (T1w) magnetic resonance imaging (MRI) for brain morphometry, yet at 7 Tesla (7 T) transmit field (B1+) inhomogeneity remains a major source of bias. Although Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) improves the tissue contrast, residual B1+ effects may persist and may be exacerbated in aging or clinical populations, where anatomical and physiological factors further challenge image quality and preprocessing. The impact of B1+ inhomogeneity on automated quality assessment and morphometric statistical inference remains insufficiently understood. Methods: Submillimeter 7 T MP2RAGE brain acquisitions from carriers of a mitochondrial gene mutation (m.3243A>G) and controls were retrieved from previous studies. Image quality before and after B1+ inhomogeneity correction was assessed by multiple automated pipelines. Case-control morphometric studies, including regional volume and mean cortical thickness, were analyzed in both registration based and deep learning based segmentation frameworks. Changes in image quality metrics (IQMs) and morphometric statistical significance were evaluated to determine the impact of B1+ inhomogeneity correction. Results: Overall image quality rating and metrics sensitive to intensity non-uniformity and topological integrity consistently improved after B1+ inhomogeneity correction. However, its impact on morphometric statistical inferences was strongly method-dependent. Some pipelines showed redistribution of significant regions, whereas others predominantly demonstrated increased effects in sensitivity. Across methods, B1+ inhomogeneity correction altered the findings of morphometric analyses, particularly in cortical regions. Conclusion: Residual B1+ inhomogeneity at 7 T substantially influences both image quality control and morphometric evaluations. Current automated quality control approaches can hardly capture these effects reliably. B1+ inhomogeneity correction will not only improve intensity uniformity, but also change sensitivity of morphometric statistical inferences. To establish reliable morphometric biomarkers at UHF strengths, explicit B1+ correction and customized preprocessing are practically necessary and highly recommended.

Functional connectivity (FC) approaches from resting-state fMRI (rs-fMRI) are amply spread to investigate the cortical organization, yet the brainstem remains relatively underexplored despite its pivotal roles in both physiological and pathological conditions. The highly collinear network, in which the strongly interconnected nodes and the widespread neuromodulatory influences induce indirect or mediated interactions, make the estimation of direct brainstem FC challenging. Standard bivariate methods fail to recover the true network structure in such complex topologies, causing false positive interactions. On the other hand, partial correlation can potentially estimate the direct FC, but multicollinearity issues and collider-induced spurious correlations limit its application in high-dimensional scenarios. Here, we propose a physiologically informed framework in which the conditioning strategy for partial correlation estimation is tailored for the investigation of the brainstem and its direct interactions within the network and with whole-brain regions. Specifically, we employed a PCA-regularized partial correlation (PCA - {rho}PC) approach, where PCA is applied to the brainstem covariates to mitigate multicollinearity and model shared modulatory variance. We show that PCA - {rho}PC improves the robustness and interpretability of brainstem FC, yielding sparser and more physiologically plausible connectomes compared with conventional (regularized) approaches. Both simulation and real fMRI data raise the possibility that Pearsons and PCA-regularized approaches may complement each other in an effort to unravel the pattern of direct vs. indirect effects in highly collinear settings, paving the way for future extensions in a wide range of multivariate neuroimaging applications.

Magnetoencephalography (MEG) measures human neural activity non-invasively with spatio-temporal precision, and has been foundational in enabling impactful discoveries in cognitive neuroscience. New on-scalp MEG sensor technologies, such as OPM-MEG, offer the opportunity to capture more information about the neuronal magnetic fields with higher sensitivity to more complex, higher order spatial components, leading to improved source localization. The accuracy of MEG and OPM-MEG source localization relies on data preprocessing techniques to isolate the neuronal fields from other magnetic and biomagnetic sources through signal space separation, rejection, and suppression methods. Current preprocessing methods risk rejecting brain signals of interest, or can spread sensor noise artifacts unknowingly. Here we propose a novel preprocessing method for MEG, and test the extent to which it overcomes limitations of prior methods. Specifically, we derive, apply, and assess a novel signal space separation (SSS) method with Fosters inverse, a weighted matrix inversion protocol that can utilize information about the MEG sensor noise and artifacts to reconstruct neuronal activity. With simulations, phantom head cryogenic MEG recordings, and subject recordings with two OPM-MEG systems, we show that Fosters inverse with SSS offers a more robust and stable reconstruction of the neuronal magnetic fields, especially in the face of sensor noise and artifacts. As the field of cognitive neuroscience continues to embrace MEG and OPM-MEG, Fosters inverse with SSS offers a robust and powerful data preprocessing technique for reducing noise and improving source localization of the underlying neuronal currents.

Population receptive field (pRF) mapping is widely used to characterize retinotopic organization based on functional magnetic resonance imaging (fMRI) data. Despite its broad adoption, the factors governing intra- and inter-subject variability in pRF estimates remain incompletely understood, limiting the ability to evaluate and optimize visual stimulation paradigms prior to data collection. Here, we investigate whether large-scale simulations can reproduce in vivo run-to-run variability patterns observed in pRF mapping and provide mechanistic insight into their origins. With GEMSim-pRF, our newly proposed computational framework for large-scale simulation and estimation of pRF responses, we generated millions of synthetic fMRI time courses across a wide range of receptive field parameters and noise conditions. We analyzed the variability of pRF estimation results derived from simulations and compared them with in vivo data from the publicly available NYU Retinotopy Dataset. Here we show that our simulation results matched the characteristic eccentricity-dependent variability observed in empirical pRF estimates. These findings show that key variability patterns observed in empirical pRF mapping can be successfully reproduced in large-scale simulations, establishing simulation-based analysis as a practical approach for understanding, predicting, evaluating and ultimately improving the behaviour of retinotopic mapping paradigms before empirical data collection.

How the activity and connectivity of the brain support consciousness remains a central question in neuroscience. Recent progress driven by the use of functional MRI has seen growing recognition that large-scale distributed functional organisation of the human and non-human primate brain are systematically and consistently reshaped by anaesthetic-induced unconsciousness, across anaesthetics and across human and macaque. Here, we generalise these results to a different primate species that is gaining traction as model organism in neuroscience, the marmoset (Callithrix jacchus). We also generalise results to an additional anaesthetic, isoflurane, which we compare with propofol and sevoflurane. We report that under anaesthesia with propofol, sevoflurane, or isoflurane, distributed brain activity from functional MRI is increasingly constrained by the underlying structural connectivity across scales. Anaesthesia also induces a collapse of the principal gradient and intrinsic functional geometry of the marmoset brain, coinciding with a breakdown of hierarchical integration. Altogether, the present results indicate generalisable signatures of anaesthesia in the large-scale organisation of the primate brain.

Abstract Optically pumped magnetometer (OPM) sensors can be placed closer to the scalp than conventional superconducting quantum interference devices (SQUID), resulting in larger magnetoencephalography (MEG) signals from neuronal activity. For event-related sensor data, such as epileptogenic activity or sensory and motor evoked responses, however, OPMs and SQUIDs often differ less in signal-to-noise ratio (SNR) than in signal magnitude. We examined two factors contributing to the relative SNR: the dependence of the signal magnitude on source depth and the effect of scalp-to-sensor distance on the noise level. Simulated MEG data for a current dipole in a spherical head model confirmed that on-scalp sensor placement delivers the largest SNR gain for superficial sources. Depending on the relative overall noise level, there may be a crossover source depth at which SNR is equal for on-scalp and off-scalp sensors and beyond which off-scalp sensors achieve higher SNR. Analysis of the equal-SNR source depth in different-sized spherical head models indicated that, for a given relative noise level, the proportion of the brain where SNR is higher in OPM than in SQUID was larger in small head models, supporting the benefits of OPMs in pediatric studies. To experimentally evaluate noise contributions of brain and non-brain origin to the SNR, we recorded somatosensory evoked fields (SEFs) at varying scalp-to-sensor distances. Generally, both the evoked response magnitude and the noise level were lower when the sensors were further away from the scalp; consequently, the SNR depended less than the signal magnitude on the scalp-to-sensor distance. Comparison of power spectral densities (PSDs) at different sensor-to-scalp distances allowed us to identify whether the dominant noise source was of brain or non-brain origin at different frequency bands. Overall, the results highlight complementary properties of OPMs vs. SQUIDs in terms of SNR, which is of interest when optimizing MEG experiments for specific subject populations and brain regions.

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.