Updating dynamic noise models with moving magnetoencephalographic (MEG) systems

Optically pumped magnetometers have opened many possibilities for the study of human brain function using wearable moveable technology. In order to fully exploit this capability, a stable low-field environment at the sensors is required. One way to achieve this is to predict (and compensate for) changes in the ambient magnetic field as the subject moves through the room. The ultimate aim is to account for dynamically changing noise environments by updating a model based on measurements from a moving sensor array. We begin by demonstrating how an appropriate environmental spatial noise model can be developed through Free-energy based model selection. We then develop a Kalman-filter based strategy to account for dynamically changing interference. We demonstrate how such a method could not only provide realistic estimates of interfering signals when the sensors are moving, but also provide powerful predictive performance (at a fixed point within the room) when both sensors and sources of interference are in motion

Magnetic Field Mapping and Correction for Moving OP-MEG

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 modelled the spatial variation in the magnetic field and used 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 showed 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

Magnetoencephalography

This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician

Seuratun kappaleen poikkeuttaminen silmänräpäysten aikana: käyttäytymis- ja neuromagneettisia havaintoja

The visual world is perceived as continuous despite frequent interruptions of sensory data due to eyeblinks and rapid eye movements. To create the perception of constancy, the brain makes use of fill-in mechanisms. This study presents an experiment in which the location of an object during smooth pursuit tracking is altered during eyeblinks. The experiment investigates the effects of blink suppression and fill-in mechanisms to cloud the discrimination of these changes. We employed a motion-tracking task, which promotes the accurate evaluation of the object’s trajectory and thus can counteract the fill-in mechanisms. Six subjects took part in the experiment, during which they were asked to report any perceived anomalies in the trajectory. Eye movements were monitored with a video-based tracking and brain responses with simultaneous MEG recordings. Discrimination success was found to depend on the direction of the displacement, and was significantly modulated by prior knowledge of the triggered effect. Eye-movement data were congruent with previous findings and revealed a smooth transition from blink recovery to object locating. MEG recordings were analysed for condition-dependent evoked and induced responses; however, intersubject variability was too large for drawing clear conclusions regarding the brain basis of the fill-in mechanisms.Visuaalinen maailma koetaan jatkuvana, vaikka silmänräpäykset ja nopeat silmänliikkeet aiheuttavat keskeytyksiä sensoriseen tiedonkeruuseen. Luodakseen käsityksen pysyvyydestä, aivot käyttävät täyttömekanismeja. Tämä tutkimus esittelee kokeen, jossa kappaleen seurantaa hitailla seurantaliikkeillä häiritään muuttamalla sen sijaintia silmänräpäysten aikana. Tämä koe tutkii, kuinka silmänräpäysten aiheuttama suppressio ja täyttömekanismit sumentavat kykyä erotella näitä muutoksia. Käytimme liikeseurantatehtävää, joka vastaavasti edistää kappaleen liikeradan tarkkaa arviointia. Kuusi koehenkilöä osallistui kokeeseen, jonka aikana heitä pyydettiin ilmoittamaan kaikki havaitut poikkeamat kappaleen liikeradassa. Silmänliikkeitä tallennettiin videopohjaisella seurannalla, ja aivovasteita yhtäaikaisella MEG:llä. Erottelykyvyn todettiin riippuvan poikkeutuksen suunnasta, sekä merkittävästi a priori tiedosta poikkeutusten esiintymistavasta. Silmänliikedata oli yhtenevää aiempien tutkimusten kanssa, ja paljasti sujuvan siirtymisen silmänräpäyksistä palautumisesta kappaleen paikallistamiseen. MEG-tallenteet analysoitiin ehdollisten heräte- ja indusoitujen vasteiden löytämiseksi, mutta yksilölliset vaste-erot koehenkilöiden välillä olivat liian suuria selkeiden johtopäätösten tekemiseksi täyttömekanismien aivoperustasta

Methodological and clinical aspects of ictal and interictal MEG

During the last years magnetoencephalography (MEG), has become an important part of the pre-surgical epilepsy workup. Interictal activity is usually recorded. Nevertheless, the technological advances now enable ictal MEG recordings as well. The records of 26 pharmaco-resistant focal epilepsy patients, who underwent ictal MEG and epilepsy surgery, were reviewed. In 12 patients prediction of ictal onset zone (IOZ) localization by ictal and interictal MEG was compared with ictal intracranial EEG (icEEG). On the lobar surface level the sensitivity of ictal MEG in IOZ location was 0.71 and the specificity 0.73. The sensitivity of the interictal MEG was 0.40 and specificity 0.77. The records of 34 operated epilepsy patients with focal cortical dysplasia (FCD) were retrospectively evaluated. The resected proportion of the source cluster related to interictal MEG was evaluated in respect to postoperative seizure outcome. 17 out of 34 patients with FCD (50%) achieved seizure freedom. The seizure outcome was similar in patients with MR-invisible and MR-visible FCD. With MEG source clusters and favorable seizure outcome (Engel class I and II) the proportion of the cluster volume resection was 49% - significantly higher (p=0.02) than with MEG clusters but unfavorable outcome (5.5% of cluster volume resection). Median nerve somatosensory evoked MEG responses were processed by movement compensation based on signal space separation (MC-SSS) and on spatio-temporal signal space separation (MC-tSSS). MEG was recorded in standard and deviant head positions. With up to 5 cm head displacement, MC-SSS decreased the mean localization error from 3.97 to 2.13 cm, but increased noise of planar gradiometers from 3.4 to 5.3 fT/cm. MC-tSSS reduced noise from 3.4 to 2.8 fT/cm and reduced the mean localization error from 3.91 to 0.89 cm. The MEG data containing speech-related artifacts and data containing alpha rhythm were processed by tSSS with different correlation limits. The speech artifact was progressively suppressed with the decreasing tSSS correlation limit. The optimal artifact suppression was achieved at correlation of 0.8. The randomly distributed source current (RDCS), and auditory and somatosensory evoked fields (AEFs and SEFs) were simulated. The information was calculated employing Shannon's theory of communication for a standard 306-sensor MEG device and for a virtual MEG helmet (VMH), which was constructed based on simulated MEG measurements in different head positions. With the simulation of 360 recorded events using RDCS model the maximum Shannon's number was 989 for single head position in standard MEG array and 1272 in VMH (28.6% additional information). With AEFs the additional contribution of VMH was 12.6% and with SEFs only 1.1%. To conclude, ictal MEG predicts IOZ location with higher sensitivity than interictal MEG. Resection of larger proportion of the MEG source cluster in patients with FCD is associated with a better seizure outcome, however, complete resection of MEG source cluster is often not required for achievement of favorable seizure outcome. The seizure outcome is similar in patients having MR-positive and MR-negative FCD. MC-tSSS decreases the source localization error to less than 1 cm, when the head is displaced up to 5 cm; however, it is reasonable to limit use of movement compensation for no more than 3-cm head displacement to keep the head inside sensor helmet. The optimization of the tSSS correlation limit to about 0.8 can improve the artifact suppression in MEG without substantial change of brain signals. MEG recording of the same brain activity in different head positions with subsequent construction of VMH can improve the information content of the data.Magnetoenkefalografia (MEG) on menetelmä, jolla mitataan aivojen tuottamia heikkoja magneettikenttiä. Yksi menetelmän tärkeimmistä kliinisistä käyttö-tarkoituksista on paikantaa epilepsiapesäkkeitä aivoissa. Tämä on tärkeää epilepsiakirurgian suunnittelussa. Potilaan liikkeet mittauksen aikana ovat aiheuttaneet epätarkkuutta pesäkkeiden paikannukseen ja häiriösignaaleja mittauksiin. Ongelma on ollut erityisen korostunut lasten mittauksissa ja epileptisten kohtausten rekisteröinneissä. Useimmissa potilaissa MEG-paikannus onkin perustunut kohtausten välisten epileptiformisten aivosähköilmiöiden paikannukseen. Pitkät MEG-rekisteröinnit ovat myös olleet haastavia koska yhteistyökykyisten potilaidenkin on vaikea olla liikkumatta pitkiä aikoja. Viime vuosien tekninen kehitys on mahdollistanut MEG-mittaukset myös pään liikkeiden aikana. Myös aivosignaalien ja kehossa olevien magneettisten materiaalien (esim hammaspaikat, sydämen tahdistimet tai aivostimulaattorit) aiheuttamien magneettisten häiriöiden erottaminen on nykyisin toteutettavissa. Tämä kehitys on mahdollistanut MEG-mittaukset potilailla, joilla aiemmin ei ollut mahdollisuutta hyötyä MEG-paikannuksista ja myös MEG-mittaukset epileptisten kohtausten aikana. Tärkeä osa väitöskirjaa on epilepsiakohtausten aikaisten MEG-mittausten kliinisen hyödyn arviointi. Tulokset osoittavat, että kohtauksenaikaiset MEG-mittaukset paikantavat herkemmin epilepsiakohtauksen lähdealueen aivoissa kuin kohtausten välisten epilepsiailmiöiden lähdepaikannus. Lähdealueiden paikannus on yhtä tarkka sekä aivokuoren pinnalla että 4 cm syvyydessä aivouurteissa. Pää ei kuitenkaan saisi liikkua 3 cm enempää MEG-mittauksen aikana, ja menetelmän herkkyys paranee oilennaisesti magneettikenttien matemaattiseen mallinnukseen perustuvalla magneettisten liikehäiriöiden poistolla. Väitöskirja tutkii lisäksi aivokuoren rakennemuutosten (paikallinen aivokuoridysplasia) aiheuttaman epilepsian kohtausten välisiä MEG-mittauksia. Päinvastoin kuin aiemmin on väitetty, ei aina ole tarpeen poistaa koko epileptisia lähdealueita sisältävää aivojen aluetta hyvän leikkaustuloksen saamiseksi. Väitöskirja esittelee myös laskennallisen MEG-anturiston määritysmenetelmän , joka lisää MEG-mittausten informaatiosisältöä huomioimalla pään liikkeet tulosten analyysissä

Dynamic causal modelling of eye movements during pursuit: Confirming precision-encoding in V1 using MEG

This paper shows that it is possible to estimate the subjective precision (inverse variance) of Bayesian beliefs during oculomotor pursuit. Subjects viewed a sinusoidal target, with or without random fluctuations in its motion. Eye trajectories and magnetoencephalographic (MEG) data were recorded concurrently. The target was periodically occluded, such that its reappearance caused a visual evoked response field (ERF). Dynamic causal modelling (DCM) was used to fit models of eye trajectories and the ERFs. The DCM for pursuit was based on predictive coding and active inference, and predicts subjects' eye movements based on their (subjective) Bayesian beliefs about target (and eye) motion. The precisions of these hierarchical beliefs can be inferred from behavioural (pursuit) data. The DCM for MEG data used an established biophysical model of neuronal activity that includes parameters for the gain of superficial pyramidal cells, which is thought to encode precision at the neuronal level. Previous studies (using DCM of pursuit data) suggest that noisy target motion increases subjective precision at the sensory level: i.e., subjects attend more to the target's sensory attributes. We compared (noisy motion-induced) changes in the synaptic gain based on the modelling of MEG data to changes in subjective precision estimated using the pursuit data. We demonstrate that imprecise target motion increases the gain of superficial pyramidal cells in V1 (across subjects). Furthermore, increases in sensory precision – inferred by our behavioural DCM – correlate with the increase in gain in V1, across subjects. This is a step towards a fully integrated model of brain computations, cortical responses and behaviour that may provide a useful clinical tool in conditions like schizophrenia

Magnetic Field Mapping and Correction for Moving OP-MEG

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

Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research

The Organization for Human Brain Mapping (OHBM) has been active in advocating for the instantiation of best practices in neuroimaging data acquisition, analysis, reporting and sharing of both data and analysis code to deal with issues in science related to reproducibility and replicability. Here we summarize recommendations for such practices in magnetoencephalographic (MEG) and electroencephalographic (EEG) research, recently developed by the OHBM neuroimaging community known by the abbreviated name of COBIDAS MEEG. We discuss the rationale for the guidelines and their general content, which encompass many topics under active discussion in the field. We highlight future opportunities and challenges to maximizing the sharing and exploitation of MEG and EEG data, and we also discuss how this ‘living’ set of guidelines will evolve to continually address new developments in neurophysiological assessment methods and multimodal integration of neurophysiological data with other data types.Peer reviewe

Neural representation of movement tau

A fundamental aspect of goal‐directed behaviour concerns the closure of motion‐gaps in a timely fashion. An influential theory about how this can be achieved is provided by the tautheory (Lee, 1998). Tau is defined as the ratio of the current distance‐to‐goal gap over the current instantaneous speed towards the goal. In this work we investigated the neural representation of tau in two sets of experiments. In one study we recorded neuromagnetic fluxes (using magnetoencephalography, MEG) from the whole brain of human subjects performing discrete hand movements aimed to targets in space, whereas the other study involved recordings of single cell activity from prefrontal and posterior parietal areas of a behaving monkey during geometrical shape‐copying tasks. These two studies provided complementary information, for the former covered the whole brain (at the cost of weak localization), whereas the latter used the finest neural grain (at the expense of limited brain regions). However, the two studies together yielded valuable information concerning the dynamic, time‐varying neural representation of tau, with respect to both integrated synaptic events in neuronal ensembles (recorded by MEG) and neural spike outputs (recorded by microelectrodes). The relations between neural signals and tau were analyzed using a linear regression model where the time‐varying neural signal (magnetic field strength in fT or spike density function) was the dependent variable and the corresponding value of movement tau and speed were the independent variables. In addition, the model included an autoregressive term to account for the expected correlated errors, given the time series nature of the data. The neurophysiological study revealed a statistically significant (p < 0.05) relation of spike density function to tau (in the presence or absence of a significant speed effect) in 17% of cells in the posterior parietal cortex (N = 399) and 8% of cells in the prefrontal cortex (N = 163). These results are in accord with previous findings in an interception task. The MEG study revealed that a mean of 21.98 (± 6.08) % of sensor signals had a statistically significant (p < 0.05) relation to tau across all subjects. These effects were distributed predominantly over the left parietal‐temporo‐occipital sensor space, with additional foci over the frontal sensorimotor regions. Altogether, these findings demonstrate a specific involvement of neurons and neuronal ensembles with the tau variable and pave the way for further studies on predictive tau control

A Comprehensive Review of Magnetoencephalography (MEG) Studies for Brain Functionality in Healthy Aging and Alzheimer's Disease (AD)

Neural oscillations were established with their association with neurophysiological activities and the altered rhythmic patterns are believed to be linked directly to the progression of cognitive decline. Magnetoencephalography (MEG) is a non-invasive technique to record such neuronal activity due to excellent temporal and fair amount of spatial resolution. Single channel, connectivity as well as brain network analysis using MEG data in resting state and task-based experiments were analyzed from existing literature. Single channel analysis studies reported a less complex, more regular and predictable oscillations in Alzheimer's disease (AD) primarily in the left parietal, temporal and occipital regions. Investigations on both functional connectivity (FC) and effective (EC) connectivity analysis demonstrated a loss of connectivity in AD compared to healthy control (HC) subjects found in higher frequency bands. It has been reported from multiplex network of MEG study in AD in the affected regions of hippocampus, posterior default mode network (DMN) and occipital areas, however, conclusions cannot be drawn due to limited availability of clinical literature. Potential utilization of high spatial resolution in MEG likely to provide information related to in-depth brain functioning and underlying factors responsible for changes in neuronal waves in AD. This review is a comprehensive report to investigate diagnostic biomarkers for AD may be identified by from MEG data. It is also important to note that MEG data can also be utilized for the same pursuit in combination with other imaging modalities