Biology subjects

Parkkonen, L.

Publications and source records attributed to Parkkonen, L..

At least 19 recordsLinked to original sources

bioRxiv2026

Action-related brain dynamics during leader-follower coordination

Leader-follower dynamics are essential for social interaction, enabling smooth inter-personal coordination. Although neuroimaging studies have identified brain networks associated with leading and following, the dynamical brain signatures supporting these roles remain poorly characterized. Here, sixteen dyads performed a joint-improvisation mirror-game task with predefined leader-follower roles while magnetoencephalography (MEG) and hand kinematics (acceleration) were simultaneously recorded. We investigated role-specific modulations of source-space MEG activity in delta (0.54 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (40-90 Hz) frequency bands, as well as coherence between cortical activity and movement kinematics. Compared with leaders, followers exhibited stronger theta-band activity in a cluster comprising frontal, cingulate, and sensorimotor regions, and stronger coherence between movement kinematics and occipital cortical activity, consistent with enhanced monitoring, adaptation, and reliance on visual kinematic information. In contrast, leaders showed reduced alpha-band power in sensorimotor and premotor regions, and stronger beta-band activity in occipital cortices, likely reflecting greater demands for action planning and predictive control. Our results identify complementary spectral and action-related brain signatures associated with leader-follower coordination during joint improvisation, providing new insights into the neural underpinnings of real-time interpersonal coordination.

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bioRxiv2026

Interpretable Decoding of Frequency-Resolved Functional Connectivity

Whole-brain functional connectivity, estimated from magnetoencephalography (MEG) data, provides a compact representation of long-range neuronal communication, making it suitable for predictive biomarker discovery. In this work, we propose a deep learning framework (FC-CNN) for predicting brain states from frequency-resolved functional connectivity estimates derived from resting-state MEG recordings. We systematically compare the performance of FC-CNN to that of conventional regression methods using amplitude and phase-based functional connectivity in the well-studied age-prediction task on the Cam-CAN cohort (n=576). We show that FC-CNN outperforms conventional approaches, and that, compared to phase synchronization, amplitude envelope correlation consistently leads to higher prediction performance. Moreover, we present quantitative evidence that the weights of a trained deep learning model can enable neurophysiological interpretation of the activity patterns that inform successful predictions. Our work demonstrates that the proposed approach successfully decodes brain states from MEG functional connectivity and is promising for discovery of predictive biomarkers for brain disorders.

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bioRxiv2026

Non-invasive tracking of hippocampal theta oscillations

Hippocampal theta oscillations play a crucial role in the formation of episodic memories by binding multisensory information into coherent episodes. Brain stimulation studies suggest that memory performance can be modulated by targeting these oscillations, offering a potential way of treating memory disorders. However, to effectively engage these rhythms, precise knowledge about their presence, frequency, phase and location is required, which has been a challenge, particularly for non-invasive methods such as magnetoencephalography (MEG). Here, we first asked if hippocampal signals are detectable in MEG recordings. Using simultaneous MEG-intracranial EEG (iEEG) data, we show that invasively recorded hippocampal activity is reflected in MEG sensor signals, demonstrating the feasibility of detecting deep-brain activity non-invasively. Building on this, we introduce a MEG-based analysis pipeline to track hippocampal theta frequency over time. Applied across three independent datasets, the pipeline captured characteristic hippocampal theta frequency patterns in both rodents and humans, and MEG-derived hippocampal frequencies were consistent with those observed simultaneously in intracranial EEG. These findings provide evidence that MEG can reliably track individual hippocampal oscillatory dynamics, paving the way for future non-invasive closed-loop interventions that adapt stimulation frequency and timing to ongoing oscillations.

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bioRxiv2025

Inter-brain functional connectivity: Are we measuring the right thing?

Hyperscanning--the simultaneous recording of brain activity from multiple individuals-- and the study of inter-brain synchronization is gaining popularity in social neuroscience. MEG/EEG hyperscanning studies often estimate inter-brain functional connectivity using phase-based metrics applied to oscillatory brain signals, assuming matching peak frequencies between the individuals studied. However, in reality, peak frequencies typically differ between subjects and between brain regions. Using simulated MEG/EEG signals, we systematically assessed how inter-individual frequency differences affect commonly used connectivity measures. Phase-based metrics were highly sensitive to frequency differences across individuals, whereas amplitude envelope correlation remained robust, offering more reliable connectivity estimates. Our results underscore the need for connectivity metrics specifically tailored to inter-brain analyses. These findings are relevant to a range of disciplines that are increasingly integrating hyperscanning into their methodological toolkits.

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bioRxiv2025

Cortical functional connectivity across the adult lifespan and its relation to sensorimotor integration

The operation of the human brain relies on functional networks enabled by inter-areal oscillatory synchronization between neuronal populations. Although disruptions in this functional connectivity are associated with brain disorders, evidence on its healthy age-dependent variation and behavioral relevance remains limited. Utilizing magnetoencephalography (MEG) recordings from 576 adults aged 18-87 years, we investigated the evolution of resting-state functional connectivity (rs-FC) across the healthy adult lifespan. We observed age-related, frequency-specific changes in widespread cortical networks. Alpha-band (8-13 Hz) rs-FC decreased, while delta (1-4 Hz), theta (4-8 Hz), and gamma-band (40-90 Hz) rs-FC increased with age. Beta-band (13-30 Hz) rs-FC followed a non-linear trajectory, peaking in middle age. The global delta, theta, alpha, and beta-band patterns differed from concurrent changes in oscillatory power, underscoring their dissociable contributions. Notably, reduced beta-band rs-FC was associated with increased sensorimotor attenuation, indicating that changes in rs-FC are behaviorally relevant for sensorimotor function. These findings advance our understanding of healthy brain aging and highlight a link between resting-state brain activity and sensorimotor integration. Key pointsO_LIFunctional connectivity is altered across the healthy adult lifespan in a frequency-dependent manner C_LIO_LIChanges in source power do not explain global functional connectivity trajectories C_LIO_LIBeta-band connectivity at rest is associated with sensorimotor attenuation independent of age-related effects C_LI

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bioRxiv2024

Comparing MEG and EEG measurement set-ups for a brain--computer interface based on selective auditory attention

Auditory attention modulates auditory evoked responses to target vs. non-target sounds in electro- and magnetoencephalographic (EEG/MEG) recordings. Employing whole-scalp MEG recordings and offline classification algorithms has been shown to enable high accuracy in tracking the target of auditory attention. Here, we investigated the decrease in accuracy when moving from the whole-scalp MEG to lower channel count EEG recordings and when training the classifier only from the initial part of the recording instead of extracting training samples throughout the recording. To this end, we recorded simultaneous MEG (306 channels) and EEG (64 channels) in 18 healthy volunteers while they were presented with concurrent streams of spoken "Yes"/"No" words and instructed to attend to one of them. We then trained support vector machine classifiers for predicting the target of attention from unaveraged trials of MEG/EEG. Classifiers were trained either on 204 MEG gradiometers or on EEG with 64, 30, 9 or 3 channels and with samples extracted randomly across or only from the beginning of the recording. The highest classification accuracy, 73% on average across the subjects for 1.0-s trials, was obtained with MEG when the training samples were randomly extracted throughout the recording. With EEG, the accuracies were 69%, 69%, 67%, and 63% when using 64, 30, 9, and 3 channels, respectively. When training the classifiers with the same amount of data but extracted only from the beginning of the recording, the accuracy dropped by 12 %-units on average, causing the result from the 3-channel EEG to fall below the chance level. Combining five consecutive trials partially compensated for this drop such that it was 1-8 %-units. While moving from whole-scalp MEG to EEG reduces classification accuracy, a usable auditory-attention-based brain-computer interfaces can be implemented with a small set of optimally-placed EEG channels.

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bioRxiv2023

Early stage of life is characterized by increased excitability of the auditory cortex in both humans and rats

Auditory evoked response (ER) undergoes notable changes during childhood and likely reflects changes in synaptic signaling in the auditory cortex. Establishing the interspecies generalizability of electrophysiological indicators of cortical maturation would offer a means to better understand the neurodevelopment of the human brain. We measured cortical ERs to simple auditory probes in three age groups of human subjects and juvenile rats. These two species exhibited a remarkably similar long-latency (150-350 ms poststimulus) response in the auditory cortex, specifically pronounced in the younger individuals in both species. An age-dependent pattern of activity was evident at the level of single trials, and the late response showed stronger trial-by-trial stability in children than the early, adult-like 100-ms response, especially in humans. This robust development-related pattern of sensory cortex excitability is likely to represent a distinct synaptic event and may be a marker of the maturational stage, especially in GABAergic cortical circuits.

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bioRxiv2023

Spectral signatures of cross-modal attentional control in the adolescent brain and their link with physical activity and aerobic fitness levels

Top-down attentional control seems to increase and suppress the activity of sensory cortices for relevant stimuli and to suppress activity for irrelevant ones. Higher physical activity (PA) and aerobic fitness (AF) levels have been associated with improved attention, but most studies have focused on unimodal tasks (e.g., visual stimuli only). The impact of higher PA or AF levels on the ability of developing brains to focus on certain stimuli while ignoring distractions remains unknown. The aim of this study was to examine the neural processes in visual and auditory sensory cortices during a cross-modal attention-allocation task using magnetoencephalography in 13-16-year-old adolescents (n = 51). During continuous and simultaneous visual (15 Hz) and auditory (40 Hz) noise-tagging stimulation, participants attended to either visual or auditory targets appearing on their left or right sides. High and low PA groups were formed based on seven-day accelerometer measurements, and high and low AF groups were determined based on the 20-m shuttle-run test. Steady-state (evoked) responses to the visual stimulus were observed in all the adolescents in the primary visual cortex, but some did not show responses in the primary auditory cortices to the auditory stimulus. The adolescents with auditory-tag-driven signals in the left temporal cortex were older than those who did not show responses. Visual cortices showed enhanced visual-tag-related activity with attention, but there was no cross-modal effect, perhaps due to the developmental effect observed in the temporal areas. The visual-tag-related responses in the occipital cortex were enhanced in the higher-PA group, irrespective of task demands. In summary, sensory cortices are unequally involved in cross-modal attention in the adolescent brain. This involvement seems to be enhanced by attention. Higher PA seems to be associated with a specific visual engagement benefit in the adolescent brain. Highlights- Visual and auditory cortices engagement differs in cross-modal processing in adolescence. - Adolescents with responses in the left temporal cortex are older than those without responses. - Physical activity, but not aerobic fitness, is associated with visual engagement benefits in the adolescent brain.

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bioRxiv2022

Imaging cortical engagement during motor imagery, mental arithmetic, and silent word generation using MEG beta rhythm

Accurate mapping of cortical engagement during mental imagery or cognitive tasks remains a challenging brain-imaging problem with immediate relevance to the development of brain-computer interfaces (BCI). We analyzed data from fourteen individuals who performed cued motor imagery, mental arithmetic, or silent word generation tasks during MEG recordings. During the motor imagery task, participants imagined the movement of either both hands (HANDS) or both feet (FEET) after the appearance of a static visual cue. During the cognitive task, participants either mentally subtracted two numbers that were displayed on the screen (SUB) or generated words starting with a letter cue that was presented (WORD). The MEG recordings were denoised using a combination of spatiotemporal filtering, the elimination of noisy epochs, and ICA decomposition. Cortical source power in the beta-band (17-25 Hz) was estimated from the selected temporal windows using a frequency-resolved beamforming method applied to the sensor-level MEG signals. The task-related cortical engagement was inferred from beta power decrements within non-overlapping 400 ms temporal windows between 400 and 2800 ms after cue presentation relative to a baseline 400 ms temporal window before cue onset. We estimated the significance of these power changes within each parcel of the Desikan-Killiany atlas using a non-parametric permutation test at the group level. During the HANDS and FEET movement-imagery conditions, beta-power decreased in premotor and motor areas, consistent with a robust engagement of these cortical regions during motor imagery. During WORD and SUB tasks, beta-power decrements signaling cortical engagement were lateralized to left hemispheric brain areas that are expected to engage in language and arithmetic processing within the temporal (superior temporal gyrus), parietal (supramarginal gyrus), and (inferior) frontal regions. A leave-one-subject-out cross-validation using a support vector machine (SVM) applied to beta power decrements across brain parcels yielded accuracy rates of 74% and 68% respectively, for classifying motor-imagery (HANDS-vs-FEET) and cognitive (WORD-vs-SUB) tasks. From the motor-versus-nonmotor contrasts, accuracy rates of 85% and 80% respectively, were observed for HAND-vs-WORD and HAND-vs-SUB. A multivariate Gaussian process classifier (GPC) provided an accuracy rate of 60% for a four-way (HANDS-FEET-WORD-SUB) classification problem. The regions identified by both SVM and GPC classification weight maps were largely consistent with the source modeling findings. Within-subject correlations of beta-decrements during the two task sessions provided insights into the level of engagement by individual subjects and showed moderately high correlations for most subjects. Our results show that it is possible to map the dynamics of cortical engagement during mental processes in the absence of dynamic sensory stimuli or overt behavioral outputs using task-related beta-power decrements. The ability to do so with the high spatiotemporal resolution afforded by MEG could potentially help better characterize the physiological basis of motor or cognitive impairments in neurological disorders and guide strategies for neurorehabilitation.

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bioRxiv2020

Joint estimation of neuralsources and their functional connections from MEG data

Current techniques to estimate directed functional connectivity from magnetoencephalography (MEG) signals involve two sequential steps; 1) Estimation of the sources and their amplitude time series from the MEG data by solving the inverse problem, and 2) fitting a multivariate autoregressive (MVAR) model to these time series for the estimation of AR coefficients, which reflect the directed interactions between the sources. However, such a sequential approach is not optimal since i) source estimation algorithms typically assume that the sources are independent, ii) the information provided by the connectivity structure is not used to inform the estimation of source amplitudes, and iii) the limited spatial resolution of source estimates often leads to spurious connectivity due to spatial leakage. Here, we present an algorithm to jointly estimate the source and connectivity parameters using Bayesian filtering, which does not require anatomical constraints in form of structural connectivity or a-priori specified regions-of-interest. By formulating a state-space model for the locations and amplitudes of a given number of sources, we show that estimation of functional connectivity can be reduced to a system identification problem. We derive a solution to this problem using a variant of the expectation-maximization (EM) algorithm known as stochastic approximation EM (SAEM). Compared to the traditional two-step approach, the joint approach using the SAEM algorithm provides a more accurate reconstruction of connectivity parameters, which we show with a connectivity benchmark simulation as well as with an electrocorticography-based simulation of MEG data. Using real MEG responses to visually presented faces in 16 subjects, we also demonstrate that our method gives source and connectivity estimates that are both physiologically plausible and largely consistent across subjects. In conclusion, the proposed joint-estimation approach based on the SAEM algorithm outperforms the traditional two-step approach in determining functional connectivity structure in MEG data.

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bioRxiv2020

Musical skills can be decoded from magnetic resonance images

Learning induces structural changes in the brain. Especially repeated, long-term behaviors, such as extensive training of playing a musical instrument, are likely to produce characteristic features to brain structure. However, it is not clear to what extent such structural features can be extracted from magnetic resonance images of the brain. Here we show that it is possible to predict whether a person is a musician or a non-musician based on the thickness of the cerebral cortex measured at 148 brain regions en-compassing the whole cortex. Using a supervised machine-learning technique, we achieved a significant ({kappa} = 0.321, p < 0.001) agreement between the actual and predicted participant groups of 30 musicians and 85 non-musicians. The areas contributing to the prediction were mostly in the frontal, parietal, and occipital lobes of the left hemisphere. Our results suggest that decoding musicianship from magnetic resonance images of brain structure is feasible. Further, the distribution of the areas that were informative in the classification, which mostly, but not entirely, overlapped with earlier findings on areas relevant for musical skills, implies that decoding-based analyses of structural properties of the brain can reveal novel aspects of musical aptitude. In particular, our results highlight differences in visual areas in addition to the already more established differences located in motor networks and networks of higher-order cognitive function.

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bioRxiv2020

Activity level in left auditory cortex predicts behavioral performance in inhibition tasks in children

Sensory processing during development is important for the emerging cognitive skills underlying goal-directed behavior. Yet, it is not known how auditory processing in children is related to their cognitive functions. Here, we utilized combined magneto- and electroencephalographic (M/EEG) measurements to show that child-unique auditory cortical activity at [~]250 ms after auditory stimulation predicts the performance in inhibition tasks. While unaffected by task demands, the amplitude of the left-hemisphere activation pattern was significantly correlated with the variability of behavioral response time. Since this activation pattern is not present in adults, our results suggest divergent brain mechanisms in adults and children for consistent performance in auditory-based cognitive tasks. This difference can be explained as a shift in cortical resources for cognitive control from sensorimotor associations in the auditory cortex of children to top-down regulated control processes involving (pre)frontal and cingulate areas in adults.

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bioRxiv2019

PCA-based source-space contrast maps reveal psychologically meaningful individual differences in continuous MEG activity

Within the field of neuroimaging, there has been an increasing trend towards studying brain activity in naturalistic conditions, and it is possible to robustly estimate networks of on-going oscillatory activity in the brain. However, not many studies have focused on differences between individuals in on-going brain activity that would be associable to psychological or behavioral characteristics. Existing standard methods can perform well at single-participant level, but generalizing the methodology across many participants is challenging due to individual differences of brains. As an example of a clinically relevant, naturalistic condition we consider here mindfulness. Trait mindfulness, as well as a mindfulness-based intervention cultivating focused attention, is often associated with benefits for psychological health. Therefore, the manner in which the brain engages in focused attention vs. mind wandering is likely to associate with individual differences in psycho-behavioral tendencies.\n\nWe recorded MEG from 29 participants both in a state of focused attention and in a state of simulated mind wandering. We used Principal Component Analysis to decompose spatial average activation maps of focused attention contrasted with two different mind wandering states. The first principal component, which reflected differential engagement of bilateral parietal areas during focused attention vs. mind wandering, was associated with behavioral characteristics of inhibition, anxiousness and depression, as measured by standard questionnaires. We demonstrated that such decomposition of time-averaged contrast maps can overcome some of the challenges in methods based on concatenated data, especially from the perspective of behaviorally and clinically relevant characteristics in the ongoing brain oscillatory activity.\n\nHighlightsO_LIWe present a specific method to analyse/establish associations between brain oscillations and behavioral characteristics.\nC_LIO_LIWe found that activity levels in parietal areas during mind wandering compared to focused attention were associated with the behavioral trait of inhibition and anxiety.\nC_LI

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bioRxiv2019

Comparison of beamformer implementations for MEG source localization

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (FieldTrip, SPM12, Brainstorm, and MNE-Python) using datasets both with and without SSS interference suppression.\n\nWe analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with SNR in all four toolboxes.\n\nWhen applied carefully to MEG data with a typical SNR (3-15 dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs. We also found that the SNR improvement offered by SSS led to more accurate localization.

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bioRxiv2019

Atypical brain-to-brain synchronization during listening to continuous natural speech in dyslexia

Listening to speech elicits brain activity time-locked to the speech sounds. This so-called neural entrainment to speech was found to be atypical in dyslexia, a reading impairment associated with neural speech processing deficits. We hypothesized that the brain responses of dyslexic vs. normal readers to real-life speech would be different, and thus the strength of inter-subject correlation (ISC) would differ from that of typical readers and be reflected in reading-related measures.\n\nWe recorded magnetoencephalograms (MEG) of 23 dyslexic and 21 typically-reading adults during listening to [~]10 min of natural Finnish speech consisting of excerpts from radio news, a podcast, a self-recorded audiobook chapter and small talk. The amplitude envelopes of band-pass-filtered MEG source signals were correlated between subjects in a cortically-constrained source space in six frequency bands. The resulting ISCs of dyslexic and typical readers were compared with a permutation-based t-test. Neuropsychological measures of phonological processing, technical reading, and working memory were correlated with the ISCs utilizing the Mantel test.\n\nDuring listening to speech, ISCs were reduced in dyslexic compared to typical readers in delta (0.5-4 Hz), alpha (8-12 Hz), low gamma (25-45 Hz) and high gamma (55-90 Hz) frequency bands. In the beta (12-25 Hz) band, dyslexics had mainly enhanced ISC to speech compared to controls. Furthermore, we found that ISCs across both groups were associated with phonological processing, technical reading, and working memory.\n\nThe atypical ISC to natural speech in dyslexics supports the temporal sampling deficit theory of dyslexia. It also suggests over-synchronization to phoneme-rate information in speech, which could indicate more effort-demanding sampling of phonemes from speech in dyslexia. These irregularities in parsing speech are likely some of the complex neural factors contributing to dyslexia. The associations between neural coupling and reading-related skills further support this notion.\n\nResearch HighlightsO_LIMEG inter-subject correlation (ISC) of dyslexics was atypical while listening to speech.\nC_LIO_LIDepending on the frequency band, dyslexics had stronger or weaker ISC than controls.\nC_LIO_LIReading-related measures correlated with the strength of ISC.\nC_LI

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bioRxiv2019

Potential of on-scalp MEG: Robust detection of human visual gamma-band responses

Electrophysiological signals recorded intracranially show rich frequency content spanning from near-DC to hundreds of hertz. Noninvasive electromagnetic signals measured with electroencephalography (EEG) or magnetoencephalography (MEG) typically contain less signal power in high frequencies than invasive recordings. Particularly, noninvasive detection of gamma-band activity (> 30 Hz) is challenging since coherently active source areas are small at such frequencies and the available imaging methods have limited spatial resolution. Compared to EEG and conventional SQUID-based MEG, on-scalp MEG should provide substantially improved spatial resolution, making it an attractive method for detecting gamma-band activity.\n\nUsing an on-scalp array comprised of eight optically-pumped magnetometers (OPMs) and a conventional whole-head SQUID array, we measured responses to a dynamic visual stimulus known to elicit strong gamma-band responses. OPMs had substantially higher signal power than SQUIDs, and had a slightly larger relative gamma-power increase over the baseline. With only eight OPMs, we could obtain gamma-activity source estimates comparable to those of SQUIDs at the group level.\n\nOur results show the feasibility of OPMs to measure gamma-band activity. To further facilitate the noninvasive detection of gamma-band activity, the on-scalp OPM arrays should be optimized with respect to sensor noise, the number of sensors and inter-sensor spacing.

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bioRxiv2019

Target of selective auditory attention can be robustly followed with MEG

Selective auditory attention enables filtering of relevant acoustic information from irrelevant. Specific auditory responses, measurable by magneto- and electroencephalography (MEG/EEG), are known to be modulated by attention to the evoking stimuli. However, such attention effects have typically been studied in unnatural conditions (e.g. during dichotic listening of pure tones) and have been demonstrated mostly in averaged auditory evoked responses. To test how reliably we can detect the attention target from unaveraged brain responses, we recorded MEG data from 15 healthy subjects that were presented with two human speakers uttering continuously the words "Yes" and "No" in an interleaved manner. The subjects were asked to attend to one speaker. To investigate which temporal and spatial aspects of the responses carry the most information about the target of auditory attention, we performed spatially and temporally resolved classification of the unaveraged MEG responses using a support vector machine. Sensor-level decoding of the responses to attended vs. unattended words resulted in a mean accuracy of 79% {+/-} 2% (N = 14) for both stimulus words. The discriminating information was mostly available 200-400 ms after the stimulus onset. Spatially-resolved source-level decoding indicated that the most informative sources were in the auditory cortices, in both the left and right hemisphere. Our result corroborates attention modulation of auditory evoked responses and shows that such modulations are detectable in unaveraged MEG responses at high accuracy, which could be exploited e.g. in an intuitive brain-computer interface.

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bioRxiv2018

Optical Co-registration of MRI and On-scalp MEG

Objective: To estimate the neural current distribution underlying magnetoencephalographic (MEG) signals and to link such estimates to brain anatomy, MEG data have to be co-registered with an anatomical image, typically an MR image. Optically-pumped magnetometers (OPMs) enable the construction of on-scalp MEG systems providing higher sensitivity and spatial resolution than conventional SQUID-based MEG systems. Here, we present a co-registration method that can be applied to on-scalp MEG systems, regardless of the number of channels. Methods: We apply a structured-light 3D scanner to create a surface mesh of the subject's head and the sensor array, which we fit to the MR image. To assess accuracy, we quantified the reproducibility of the surface mesh and localized current dipoles embedded in a phantom. Finally, we measured somatosensory evoked fields (SEF) to median nerve stimulation and compared their source estimates with those obtained with a SQUID-based MEG system. Results: The structured-light scanner reproduced the head surface with < 1 mm error. Phantom dipoles were localized with a mean error of 2.14 mm. The difference in SEF dipole positions between OPMs and SQUIDs were 5.0, 0.9, and 1.6 mm for N20m, P35m and P60m response peaks, respectively. Conclusion: The developed co-registration is inexpensive, fast and can easily be applied to on-scalp MEG. It is also more convenient to use than traditional co-registration methods while also being more accurate. Significance: We developed and validated a co-registration method that can be applied to on-scalp MEG systems. The method enables accurate source estimation with these novel MEG systems.

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