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Human Brain Mapping Jun 2020Electrophysiological signals from the cerebellum have traditionally been viewed as inaccessible to magnetoencephalography (MEG) and electroencephalography (EEG). Here,...
Electrophysiological signals from the cerebellum have traditionally been viewed as inaccessible to magnetoencephalography (MEG) and electroencephalography (EEG). Here, we challenge this position by investigating the ability of MEG and EEG to detect cerebellar activity using a model that employs a high-resolution tessellation of the cerebellar cortex. The tessellation was constructed from repetitive high-field (9.4T) structural magnetic resonance imaging (MRI) of an ex vivo human cerebellum. A boundary-element forward model was then used to simulate the M/EEG signals resulting from neural activity in the cerebellar cortex. Despite significant signal cancelation due to the highly convoluted cerebellar cortex, we found that the cerebellar signal was on average only 30-60% weaker than the cortical signal. We also made detailed M/EEG sensitivity maps and found that MEG and EEG have highly complementary sensitivity distributions over the cerebellar cortex. Based on previous fMRI studies combined with our M/EEG sensitivity maps, we discuss experimental paradigms that are likely to offer high M/EEG sensitivity to cerebellar activity. Taken together, these results show that cerebellar activity should be clearly detectable by current M/EEG systems with an appropriate experimental setup.
Topics: Cerebellar Cortex; Computer Simulation; Electroencephalography; Humans; Magnetic Resonance Imaging; Magnetoencephalography; Models, Theoretical; Transcranial Magnetic Stimulation
PubMed: 32115870
DOI: 10.1002/hbm.24951 -
Scientific Reports Apr 2022Non-invasive human brain functional imaging with millisecond resolution can be achieved only with magnetoencephalography (MEG) and electroencephalography (EEG). MEG has...
Non-invasive human brain functional imaging with millisecond resolution can be achieved only with magnetoencephalography (MEG) and electroencephalography (EEG). MEG has better spatial resolution than EEG because signal distortion due to inhomogeneous head conductivity is negligible in MEG but serious in EEG. However, this advantage has been practically limited by the necessary setback distances between the sensors and scalp, because the Dewar vessel containing liquid helium for superconducting quantum interference devices (SQUIDs) requires a thick vacuum wall. Latest developments of high critical temperature (high-T) SQUIDs or optically pumped magnetometers have allowed closer placement of MEG sensors to the scalp. Here we introduce the use of tunnel magneto-resistive (TMR) sensors for scalp-attached MEG. Improvement of TMR sensitivity with magnetic flux concentrators enabled scalp-tangential MEG at 2.6 mm above the scalp, to target the largest signal component produced by the neural current below. In a healthy subject, our single-channel TMR-MEG system clearly demonstrated the N20m, the initial cortical component of the somatosensory evoked response after median nerve stimulation. Multisite measurement confirmed a spatially and temporally steep peak of N20m, immediately above the source at a latency around 20 ms, indicating a new approach to non-invasive functional brain imaging with millimeter and millisecond resolutions.
Topics: Brain; Brain Mapping; Electroencephalography; Humans; Magnetoencephalography; Scalp
PubMed: 35414691
DOI: 10.1038/s41598-022-10155-6 -
The Journal of Neuroscience : the... Apr 2017Predicting future reward is paramount to performing an optimal action. Although a number of brain areas are known to encode such predictions, a detailed account of how...
Predicting future reward is paramount to performing an optimal action. Although a number of brain areas are known to encode such predictions, a detailed account of how the associated representations evolve over time is lacking. Here, we address this question using human magnetoencephalography (MEG) and multivariate analyses of instantaneous activity in reconstructed sources. We overtrained participants on a simple instrumental reward learning task where geometric cues predicted a distribution of possible rewards, from which a sample was revealed 2000 ms later. We show that predicted mean reward (i.e., expected value), and predicted reward variability (i.e., economic risk), are encoded distinctly. Early on, representations of mean reward are seen in parietal and visual areas, and later in frontal regions with orbitofrontal cortex emerging last. Strikingly, an encoding of reward variability emerges simultaneously in parietal/sensory and frontal sources and later than mean reward encoding. An orbitofrontal variability encoding emerged around the same time as that seen for mean reward. Crucially, cross-prediction showed that mean reward and variability representations are distinct and also revealed that instantaneous representations become more stable over time. Across sources, the best fitting metric for variability signals was coefficient of variation (rather than SD or variance), but distinct best metrics were seen for individual brain regions. Our data demonstrate how a dynamic encoding of probabilistic reward prediction unfolds in the brain both in time and space. Predicting future reward is paramount to optimal behavior. To gain insight into the underlying neural computations, we investigate how reward representations in the brain arise over time. Using magnetoencephalography, we show that a representation of predicted mean reward emerges early in parietal/sensory regions and later in frontal cortex. In contrast, predicted reward variability representations appear in most regions at the same time, and slightly later than for mean reward. For both features, representations dynamically change >1000 ms before stabilizing. The best metric for encoding variability is coefficient of variation, with heterogeneity in this encoding seen between brain areas. The results provide novel insights into the emergence of predictive reward representations.
Topics: Adult; Brain; Female; Forecasting; Humans; Magnetoencephalography; Male; Models, Statistical; Photic Stimulation; Reaction Time; Reward; Young Adult
PubMed: 28270567
DOI: 10.1523/JNEUROSCI.2943-16.2017 -
Brain and Behavior Sep 2023Empathy is a social-cognitive process that operates by relying mainly on the suppression of the cortical alpha rhythm. This phenomenon has been evidenced in dozens of...
INTRODUCTION
Empathy is a social-cognitive process that operates by relying mainly on the suppression of the cortical alpha rhythm. This phenomenon has been evidenced in dozens of electrophysiological studies targeting adult human subjects. Yet, recent neurodevelopmental studies indicated that at a younger age, empathy involves reversed brain responses (e.g., alpha enhancement patterns). In this multimodal study, we capture neural activity at the alpha range, and hemodynamic response and target subjects at approximately 20 years old as a unique time window in development that allows investigating both low-alpha suppression and high-alpha enhancement. We aim to further investigate the functional role of low-alpha power suppression and high-alpha power enhancement during empathy development.
METHODS
Brain data from 40 healthy individuals were recorded in two consecutive sessions of magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) while subjects perceived vicarious physical pain or no pain.
RESULTS
MEG revealed that the alpha pattern shift during empathy happens in an all-or-none pattern: power enhancement before 18 and suppression after 18 years of age. Additionally, MEG and fMRI highlight a correspondence between high-alpha power increase and blood-oxygen-level-dependent (BOLD) decrease before 18, but low-alpha power decrease and BOLD increase after 18. Importantly, this neurodevelopmental transition was not revealed by four other measures: self-reported (a) ratings of the task stimuli, (b) ratings of naturalistic vignettes of vicarious pain, (c) trait empathy, or neural data from (d) a control neuroimaging task.
DISCUSSION
Findings suggest that at the critical age of around 18, empathy is underpinned by an all-or-none transition from high-alpha power enhancement and functional inhibition to low-alpha power suppression and functional activation in particular brain regions, possibly indicating a marker of maturation in empathic ability. This work advances a recent neurodevelopmental line of studies and provides insight into the functional maturation of empathy at the coming of age.
Topics: Adult; Humans; Young Adult; Alpha Rhythm; Empathy; Magnetoencephalography; Brain; Magnetic Resonance Imaging; Pain
PubMed: 37334437
DOI: 10.1002/brb3.3110 -
Brain Topography Nov 2018Recent advances in magnetic sensing has made on-scalp magnetoencephalography (MEG) possible. In particular, optically-pumped magnetometers (OPMs) have reached...
Recent advances in magnetic sensing has made on-scalp magnetoencephalography (MEG) possible. In particular, optically-pumped magnetometers (OPMs) have reached sensitivity levels that enable their use in MEG. In contrast to the SQUID sensors used in current MEG systems, OPMs do not require cryogenic cooling and can thus be placed within millimetres from the head, enabling the construction of sensor arrays that conform to the shape of an individual's head. To properly estimate the location of neural sources within the brain, one must accurately know the position and orientation of sensors in relation to the head. With the adaptable on-scalp MEG sensor arrays, this coregistration becomes more challenging than in current SQUID-based MEG systems that use rigid sensor arrays. Here, we used simulations to quantify how accurately one needs to know the position and orientation of sensors in an on-scalp MEG system. The effects that different types of localisation errors have on forward modelling and source estimates obtained by minimum-norm estimation, dipole fitting, and beamforming are detailed. We found that sensor position errors generally have a larger effect than orientation errors and that these errors affect the localisation accuracy of superficial sources the most. To obtain similar or higher accuracy than with current SQUID-based MEG systems, RMS sensor position and orientation errors should be [Formula: see text] and [Formula: see text], respectively.
Topics: Brain; Brain Mapping; Humans; Magnetoencephalography; Scalp
PubMed: 29934728
DOI: 10.1007/s10548-018-0656-5 -
Proceedings of the National Academy of... Jun 2023Speech, as the spoken form of language, is fundamental for human communication. The phenomenon of covert inner speech implies functional independence of speech content...
Speech, as the spoken form of language, is fundamental for human communication. The phenomenon of covert inner speech implies functional independence of speech content and motor production. However, it remains unclear how a flexible mapping between speech content and production is achieved on the neural level. To address this, we recorded magnetoencephalography in humans performing a rule-based vocalization task. On each trial, vocalization content (one of two vowels) and production form (overt or covert) were instructed independently. Using multivariate pattern analysis, we found robust neural information about vocalization content and production, mostly originating from speech areas of the left hemisphere. Production signals dynamically transformed upon presentation of the content cue, whereas content signals remained largely stable throughout the trial. In sum, our results show dissociable neural representations of vocalization content and production in the human brain and provide insights into the neural dynamics underlying human vocalization.
Topics: Humans; Brain; Speech; Magnetoencephalography; Speech Perception; Brain Mapping
PubMed: 37253014
DOI: 10.1073/pnas.2219310120 -
PloS One 2022Optically pumped magnetometers (OPMs) have recently become so sensitive that they are suitable for use in magnetoencephalography (MEG). These sensors solve operational...
Optically pumped magnetometers (OPMs) have recently become so sensitive that they are suitable for use in magnetoencephalography (MEG). These sensors solve operational problems of the current standard MEG, where superconducting quantum interference device (SQUID) gradiometers and magnetometers are being used. The main advantage of OPMs is that they do not require cryogenics for cooling. Therefore, they can be placed closer to the scalp and are much easier to use. Here, we measured auditory evoked fields (AEFs) with both SQUID- and OPM-based MEG systems for a group of subjects to better understand the usage of a limited sensor count OPM-MEG. We present a theoretical framework that transforms the within subject data and equivalent simulation data from one MEG system to the other. This approach works on the principle of solving the inverse problem with one system, and then using the forward model to calculate the magnetic fields expected for the other system. For the source reconstruction, we used a minimum norm estimate (MNE) of the current distribution. Two different volume conductor models were compared: the homogeneous conducting sphere and the three-shell model of the head. The transformation results are characterized by a relative error and cross-correlation between the measured and the estimated magnetic field maps of the AEFs. The results for both models are encouraging. Since some commercial OPMs measure multiple components of the magnetic field simultaneously, we additionally analyzed the effect of tangential field components. Overall, our dual-axis OPM-MEG with 15 sensors yields similar information to a 62-channel SQUID-MEG with its field of view restricted to the right hemisphere.
Topics: Animals; Brain; Computer Simulation; Equipment Design; Humans; Magnetic Fields; Magnetoencephalography; Magnetometry; Optics and Photonics; Superconductivity
PubMed: 35045107
DOI: 10.1371/journal.pone.0262669 -
The European Journal of Neuroscience Nov 2022Neural oscillations often occur as transient bursts with variable amplitude and frequency dynamics. Quantifying these effects is important for understanding...
Neural oscillations often occur as transient bursts with variable amplitude and frequency dynamics. Quantifying these effects is important for understanding brain-behaviour relationships, especially in continuous datasets. To robustly measure bursts, rhythmical periods of oscillatory activity must be separated from arrhythmical background 1/f activity, which is ubiquitous in electrophysiological recordings. The Better OSCillation (BOSC) framework achieves this by defining a power threshold above the estimated background 1/f activity, combined with a duration threshold. Here we introduce a modification to this approach called fBOSC, which uses a spectral parametrisation tool to accurately model background 1/f activity in neural data. fBOSC (which is openly available as a MATLAB toolbox) is robust to power spectra with oscillatory peaks and can also model non-linear spectra. Through a series of simulations, we show that fBOSC more accurately models the 1/f power spectrum compared with existing methods. fBOSC was especially beneficial where power spectra contained a 'knee' below ~.5-10 Hz, which is typical in neural data. We also found that, unlike other methods, fBOSC was unaffected by oscillatory peaks in the neural power spectrum. Moreover, by robustly modelling background 1/f activity, the sensitivity for detecting oscillatory bursts was standardised across frequencies (e.g., theta- and alpha-bands). Finally, using openly available resting state magnetoencephalography and intracranial electrophysiology datasets, we demonstrate the application of fBOSC for oscillatory burst detection in the theta-band. These simulations and empirical analyses highlight the value of fBOSC in detecting oscillatory bursts, including in datasets that are long and continuous with no distinct experimental trials.
Topics: Magnetoencephalography; Brain; Electrophysiological Phenomena
PubMed: 36161675
DOI: 10.1111/ejn.15829 -
Sensors (Basel, Switzerland) Apr 2022Magnetoencephalography (MEG) is a neuroimaging technique that measures the magnetic fields of the brain outside of the head. In the past, the most suitable magnetometer...
Magnetoencephalography (MEG) is a neuroimaging technique that measures the magnetic fields of the brain outside of the head. In the past, the most suitable magnetometer for MEG was the superconducting quantum interference device (SQUID), but in recent years, a new type has also been used, the optically pumped magnetometer (OPM). OPMs can be configured to measure multiple directions of magnetic field simultaneously. This work explored whether combining multiple directions of the magnetic field lowers the source localization error of brain sources under various conditions of noise. We simulated dipolar-like sources for multiple configurations of both SQUID- and OPM-MEG systems. To test the performance of a given layout, we calculated the average signal-to-noise ratio and the root mean square of the simulated magnetic field; furthermore, we evaluated the performance of the dipole fit. The results showed that the field direction normal to the scalp yields a higher signal-to-noise ratio and that ambient noise has a much lower impact on its localization error; therefore, this is the optimal choice for source localization when only one direction of magnetic field can be measured. For a low number of OPMs, combining multiple field directions greatly improves the source localization results. Lastly, we showed that MEG sensors that can be placed closer to the brain are more suitable for localizing deeper sources.
Topics: Brain; Computer Simulation; Magnetoencephalography; Neuroimaging; Superconductivity
PubMed: 35590874
DOI: 10.3390/s22093184 -
IEEE Transactions on Bio-medical... Feb 2022Optically pumped magnetometers (OPMs) have made moving, wearable magnetoencephalography (MEG) possible. The OPMs typically used for MEG require a low background magnetic...
BACKGROUND
Optically pumped magnetometers (OPMs) have made moving, wearable magnetoencephalography (MEG) possible. The OPMs typically used for MEG require a low background magnetic field to operate, which is achieved using both passive and active magnetic shielding. However, the background magnetic field is never truly zero Tesla, and so the field at each of the OPMs changes as the participant moves. This leads to position and orientation dependent changes in the measurements, which manifest as low frequency artefacts in MEG data.
OBJECTIVE
We model the spatial variation in the magnetic field and use the model to predict the movement artefact found in a dataset.
METHODS
We demonstrate a method for modelling this field with a triaxial magnetometer, then show that we can use the same technique to predict the movement artefact in a real OPM-based MEG (OP-MEG) dataset.
RESULTS
Using an 86-channel OP-MEG system, we found that this modelling method maximally reduced the power spectral density of the data by 27.8 ± 0.6 dB at 0 Hz, when applied over 5 s non-overlapping windows.
CONCLUSION
The magnetic field inside our state-of-the art magnetically shielded room can be well described by low-order spherical harmonic functions. We achieved a large reduction in movement noise when we applied this model to OP-MEG data.
SIGNIFICANCE
Real-time implementation of this method could reduce passive shielding requirements for OP-MEG recording and allow the measurement of low-frequency brain activity during natural participant movement.
Topics: Artifacts; Brain; Humans; Magnetic Fields; Magnetoencephalography
PubMed: 34324421
DOI: 10.1109/TBME.2021.3100770