Spatial Filtering of Magnetoencephalographic Data in Spherical Harmonics Domain

We introduce new spatial filtering methods in the spherical harmonics domain for constraining magnetoencephalographic (MEG) multichannel measurements to user-specified sphericalregions of interests (ROI) inside the head. The main idea of the spatial filtering is to emphasize those signals arising from an ROI, while suppressing the signals coming from outsidethe ROI. We exploit a well-known method called the signal space separation (SSS), whichcan decompose MEG data into a signal component generated by neurobiological sourcesand a noise component generated by external sources outside the head. The novel methodspresented in this work, expanded SSS (exSSS) and generalized expanded SSS (genexSSS)utilize a beamspace optimization criterion in order to linearly transform the inner signal SSScoefficients to represent the sources belonging to the ROI. The filters mainly depend on theradius and the center of the ROI. The simplicity of the derived formulations of our methodsstems from the natural appropriateness to spherical domain and orthogonality properties ofthe SSS basis functions that are intimately related to the vector spherical harmonics. Thus,unlike the traditional MEG spatial filtering techniques, exSSS and genexSSS do not needany numerical computation procedures on discretized headspace. The validation and performance of the algorithms are demonstrated by experiments utilizing both simulated and realMEG data

Brain Activity Mapping from MEG Data via a Hierarchical Bayesian Algorithm with Automatic Depth Weighting

A recently proposed iterated alternating sequential (IAS) MEG inverse solver algorithm, based on the coupling of a hierarchical Bayesian model with computationally efficient Krylov subspace linear solver, has been shown to perform well for both superficial and deep brain sources. However, a systematic study of its ability to correctly identify active brain regions is still missing. We propose novel statistical protocols to quantify the performance of MEG inverse solvers, focusing in particular on how their accuracy and precision at identifying active brain regions. We use these protocols for a systematic study of the performance of the IAS MEG inverse solver, comparing it with three standard inversion methods, wMNE, dSPM, and sLORETA. To avoid the bias of anecdotal tests towards a particular algorithm, the proposed protocols are Monte Carlo sampling based, generating an ensemble of activity patches in each brain region identified in a given atlas. The performance in correctly identifying the active areas is measured by how much, on average, the reconstructed activity is concentrated in the brain region of the simulated active patch. The analysis is based on Bayes factors, interpreting the estimated current activity as data for testing the hypothesis that the active brain region is correctly identified, versus the hypothesis of any erroneous attribution. The methodology allows the presence of a single or several simultaneous activity regions, without assuming that the number of active regions is known. The testing protocols suggest that the IAS solver performs well with both with cortical and subcortical activity estimation

Randomized Multiresolution Scanning in Focal and Fast E/MEG Sensing of Brain Activity with a Variable Depth

We focus on electromagnetoencephalography imaging of the neural activity and, in particular, finding a robust estimate for the primary current distribution via the hierarchical Bayesian model (HBM). Our aim is to develop a reasonably fast maximum a posteriori (MAP) estimation technique which would be applicable for both superficial and deep areas without specific a priori knowledge of the number or location of the activity. To enable source distinguishability for any depth, we introduce a randomized multiresolution scanning (RAMUS) approach in which the MAP estimate of the brain activity is varied during the reconstruction process. RAMUS aims to provide a robust and accurate imaging outcome for the whole brain, while maintaining the computational cost on an appropriate level. The inverse gamma (IG) distribution is applied as the primary hyperprior in order to achieve an optimal performance for the deep part of the brain. In this proof-of-the-concept study, we consider the detection of simultaneous thalamic and somatosensory activity via numerically simulated data modeling the 14-20 ms post-stimulus somatosensory evoked potential and field response to electrical wrist stimulation. Both a spherical and realistic model are utilized to analyze the source reconstruction discrepancies. In the numerically examined case, RAMUS was observed to enhance the visibility of deep components and also marginalizing the random effects of the discretization and optimization without a remarkable computation cost. A robust and accurate MAP estimate for the primary current density was obtained in both superficial and deep parts of the brain.Comment: Brain Topogr (2020

Sensitivity of MEG and EEG to Source Orientation

An important difference between magnetoencephalography (MEG) and electroencephalography (EEG) is that MEG is insensitive to radially oriented sources. We quantified computationally the dependency of MEG and EEG on the source orientation using a forward model with realistic tissue boundaries. Similar to the simpler case of a spherical head model, in which MEG cannot see radial sources at all, for most cortical locations there was a source orientation to which MEG was insensitive. The median value for the ratio of the signal magnitude for the source orientation of the lowest and the highest sensitivity was 0.06 for MEG and 0.63 for EEG. The difference in the sensitivity to the source orientation is expected to contribute to systematic differences in the signal-to-noise ratio between MEG and EEG.National Institutes of Health (U.S.) (Grant NS057500)National Institutes of Health (U.S.) (Grant NS037462)National Institutes of Health (U.S.) (Grant HD040712)National Center for Research Resources (U.S.) (P41RR14075)Mind Research Networ

MEG:hen perustuvan aivo-tietokone -käyttöliittymän kehitys

Brain–computer interfaces (BCI) have recently gained interest both in basic neuroscience and clinical interventions. The majority of noninvasive BCIs measure brain activity with electroencephalography (EEG). However, the real-time signal analysis and decoding of brain activity suffer from low signal-to-noise ratio and poor spatial resolution of EEG. These limitations could be overcome by using magnetoencephalography (MEG) as an alternative measurement modality. The aim of this thesis is to develop an MEG-based BCI for decoding hand motor imagery, which could eventually serve as a therapeutic method for patients recovering from e.g. cerebral stroke. Here, machine learning methods for decoding motor imagery -related brain activity are validated with healthy subjects’ MEG measurements. The first part of the thesis (Study I) involves a comparison of feature extraction methods for classifying left- vs right-hand motor imagery (MI), and MI vs rest. It was found that spatial filtering and further extraction of bandpower features yield better classification accuracy than time–frequency features extracted from parietal gradiometers. Furthermore, prior spatial filtering improved the discrimination capability of time–frequency features. The training data for a BCI is typically collected in the beginning of each measurement session. However, as this can be time-consuming and exhausting for the subject, the training data from other subjects’ measurements could be used as well. In the second part of the thesis (Study II), methods for across-subject classification of MI were compared. The results showed that a classifier based on multi-task learning with a l2,1-norm regularized logistic regression was the best method for across-subject decoding for both MEG and EEG. In Study II, we also compared the decoding results of simultaneously measured EEG and MEG data, and investigated whether the MEG responses to passive hand movements could be used to train a classifier to detect MI. MEG yielded altogether slightly, but not significantly, better results than EEG. Training the classifiers with subject’s own or other subjects’ passive movements did not result in high accuracy, which indicates that passive movements should not be used for calibrating an MI-BCI. The methods presented in this thesis are suitable for a real-time MEG-based BCI. The decoding results can be used as a benchmark when developing other classifiers specifically for motor imagery -related MEG data.Aivo-tietokone -käyttöliittymät (brain–computer interface; BCI) ovat viime aikoina herättäneet kiinnostusta niin neurotieteen perustutkimuksessa kuin kliinisissä interventioissakin. Suurin osa ei-invasiivisista BCI:stä mittaa aivotoimintaa elektroenkefalografialla (EEG). EEG:n matala signaali-kohinasuhde ja huono avaruudellinen resoluutio kuitenkin hankaloittavat reaaliaikais-ta signaalianalyysia ja aivotoiminnan luokittelua. Nämä rajoitteet voidaan kiertää käyttämällä magnetoenkefalografiaa (MEG) vaihtoehtoisena mittausmenetelmänä. Tämän työn tavoitteena on kehittää käden liikkeen kuvittelua luokitteleva, MEG:hen perustuva BCI, jota voidaan myöhemmin käyttää terapeuttisena menetelmänä esimerkiksi aivoinfarktista toipuvien potilaiden kuntoutuk-sessa. Tutkimuksessa validoidaan terveillä koehenkilöillä tehtyjen MEG-mittausten perusteella koneoppimismenetelmiä, joilla luokitellaan liikkeen kuvittelun aiheuttamaa aivotoimintaa. Ensimmäisessä osatyössä (Tutkimus I) vertailtiin piirteenirrotusmenetelmiä, joita käytetään erottamaan toisistaan vasemman ja oikean käden kuvittelu sekä liikkeen kuvittelu ja lepotila. Ha-vaittiin, että avaruudellisesti suodatettujen signaalien taajuuskaistan teho luokittelupiirteenä tuotti parempia luokittelutarkkuuksia kuin parietaalisista gradiometreistä mitatut aika-taajuuspiirteet. Lisäksi edeltävä avaruudellinen suodatus paransi aika-taajuuspiirteiden erottelukykyä luokittelu-tehtävissä.BCI:n opetusdata kerätään yleensä kunkin mittauskerran alussa. Koska tämä voi kuitenkin olla aikaavievää ja uuvuttavaa koehenkilölle, opetusdatana voidaan käyttää myös muilta koehenkilöiltä kerättyjä mittaussignaaleja. Toisessa osatyössä (Tutkimus II) vertailtiin koehenkilöiden väliseen luo-kitteluun soveltuvia menetelmiä. Tulosten perusteella monitehtäväoppimista ja l2,1-regularisoitua logistista regressiota käyttävä luokittelija oli paras menetelmä koehenkilöiden väliseen luokitteluun sekä MEG:llä että EEG:llä. Toisessa osatyössä vertailtiin myös samanaikaisesti mitattujen MEG:n ja EEG:n tuottamia luokit-telutuloksia, sekä tutkittiin voidaanko passiivisten kädenliikkeiden aikaansaamia MEG-vasteita käyttää liikkeen kuvittelua tunnistavien luokittelijoiden opetukseen. MEG tuotti hieman, muttei merkittävästi, parempia tuloksia kuin EEG. Luokittelijoiden opetus koehenkilöiden omilla tai mui-den koehenkilöiden passiiviliikkeillä ei tuottanut hyviä luokittelutarkkuuksia, mikä osoittaa että passiiviliikkeitä ei tulisi käyttää liikkeen kuvittelua tunnistavan BCI:n kalibrointiin. Työssä esitettyjä menetelmiä voidaan käyttää reaaliaikaisessa MEG-BCI:ssä. Luokittelutuloksia voidaan käyttää vertailukohtana kehitettäessä muita liikkeen kuvitteluun liittyvän MEG-datan luokittelijoita

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

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