111 research outputs found

    Dynamic filtering of static dipoles in magnetoencephalography

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    We consider the problem of estimating neural activity from measurements of the magnetic fields recorded by magnetoencephalography. We exploit the temporal structure of the problem and model the neural current as a collection of evolving current dipoles, which appear and disappear, but whose locations are constant throughout their lifetime. This fully reflects the physiological interpretation of the model. In order to conduct inference under this proposed model, it was necessary to develop an algorithm based around state-of-the-art sequential Monte Carlo methods employing carefully designed importance distributions. Previous work employed a bootstrap filter and an artificial dynamic structure where dipoles performed a random walk in space, yielding nonphysical artefacts in the reconstructions; such artefacts are not observed when using the proposed model. The algorithm is validated with simulated data, in which it provided an average localisation error which is approximately half that of the bootstrap filter. An application to complex real data derived from a somatosensory experiment is presented. Assessment of model fit via marginal likelihood showed a clear preference for the proposed model and the associated reconstructions show better localisation

    A statistical approach to the inverse problem in magnetoencephalography

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    Magnetoencephalography (MEG) is an imaging technique used to measure the magnetic field outside the human head produced by the electrical activity inside the brain. The MEG inverse problem, identifying the location of the electrical sources from the magnetic signal measurements, is ill-posed, that is, there are an infinite number of mathematically correct solutions. Common source localization methods assume the source does not vary with time and do not provide estimates of the variability of the fitted model. Here, we reformulate the MEG inverse problem by considering time-varying locations for the sources and their electrical moments and we model their time evolution using a state space model. Based on our predictive model, we investigate the inverse problem by finding the posterior source distribution given the multiple channels of observations at each time rather than fitting fixed source parameters. Our new model is more realistic than common models and allows us to estimate the variation of the strength, orientation and position. We propose two new Monte Carlo methods based on sequential importance sampling. Unlike the usual MCMC sampling scheme, our new methods work in this situation without needing to tune a high-dimensional transition kernel which has a very high cost. The dimensionality of the unknown parameters is extremely large and the size of the data is even larger. We use Parallel Virtual Machine (PVM) to speed up the computation.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS716 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Open Database of Epileptic EEG with MRI and Postoperational Assessment of Foci—a Real World Verification for the EEG Inverse Solutions

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    This paper introduces a freely accessible database http://eeg.pl/epi, containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part of the clinical routine at the Warsaw Memorial Child Hospital. Each record contains (1) pre-surgical electroencephalography (EEG) recording (10–20 system) with inter-ictal discharges marked separately by an expert, (2) a full set of magnetic resonance imaging (MRI) scans for calculations of the realistic forward models, (3) structural placement of the epileptogenic zone, recognized by electrocorticography (ECoG) and post-surgical results, plotted on pre-surgical MRI scans in transverse, sagittal and coronal projections, (4) brief clinical description of each case. The main goal of this project is evaluation of possible improvements of localization of epileptic foci from the surface EEG recordings. These datasets offer a unique possibility for evaluating different EEG inverse solutions. We present preliminary results from a subset of these cases, including comparison of different schemes for the EEG inverse solution and preprocessing. We report also a finding which relates to the selective parametrization of single waveforms by multivariate matching pursuit, which is used in the preprocessing for the inverse solutions. It seems to offer a possibility of tracing the spatial evolution of seizures in time

    Numerical methods for improved signal to noise ratios in spatiotemporal biomedical data

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    Magnetocardiography (MCG) is a technique to measure the magnetic fields produced by electrical activity in the heart. The interpretation of MCG signals is difficult because of different disturbances and noise. Several methods have been suggested for noise reduction in MCG data such as averaging, pass or stop band filters, and statistical based methods, but a unified framework that takes into account different typologies of MCG signals (rest, stress, and patients with an already ICD– Implanted Cardioverter Defibrillator- implanted) using an adequate number of recordings is still missing. Consequently, the main aim of the thesis is to develop methods for noise and artifacts treatment. Due to the non-stationarity (NS) of the noise, the conventional ensemble averaging of the data does not yield the theoretical improvement. In order to overcome this problem an average procedure that ignores the noisiest beats is applied. The results of this averaging procedure confirms that in case of NS, the Signal to Noise Ratio (SNR) does not behave as expected, but reaches a maximum after a certain number of selected beats. Furthermore, a theoretical proof of this result is given. The second part of the thesis deals with techniques based on Blind Source Separation (BSS), as preprocessing step for the averaging procedure, in case of MCG signals with low SNR. Different BSS algorithms are compared in order to find the best one in terms of noise reduction, separation, and computational time for each data typology. A drawback of BSS techniques is the order of the sources that cannot be determined a priori; for this reason 3 methods (based on different statistical principles) have been developed for the retrieval of cardiac signals. The last part of the thesis deals with the application of BSS methods to a category of signals not yet analyzed: patients with ICD implanted. It is shown that it is possible to extract the cardiac signal also in such noisy data, although not automatically. The Temporal Decorrelation source SEParation (TDSEP) algorithm outperforms the other BSS methods. This thesis shows that, applying novel automatic routines for the removal of noise and artifacts, MCG data could be used in clinical environments

    Advanced Source Reconstruction and Volume Conductor Modeling for Fetal Magnetocardiography

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    Abstract Fetuses that are identified with cardiac hypotrophy, hypertension and metabolic anomalies have higher risk of suffering from various health problems in their later life. Therefore, the early detection of congenital heart anomalies is critical for monitoring or prompt interventions, which can reduce the risks of congestive heart failure. Compared to adult cardiac monitoring, fetal electrophysiological heart monitoring using fetal ECG is extremely difficult due to the low signal amplitude and interferences from the maternal cardiac signal and to the complex environment inside the mother's womb. This problem is even worse in conditions such as diabetic pregnancies because of further signal reduction due to maternal obesity. At the same time, the prevalence of congenital heart anomalies is higher for fetuses of diabetic mothers. The purpose of this thesis is to develop and test fetal magnetocardiography (fMCG) techniques as an alternative diagnostic tool for the detection and monitoring of the fetal heart. fMCG is a novel technique that records the magnetic fields generated by the fetal heart's electric activity. From the aspect of signal processing, magnetic signals generated by the fetal heart are less affected by the low electrical conductivity of the surrounding fetal and maternal tissues compared to the electric signals recorded over the maternal abdomen, and can provide reliable recordings as early as 12 weeks of gestation. However, the fetal heart signals recorded with an array of magnetic sensors at a small distance from the maternal abdomen are affected by the source-to-sensor distance as well as by the geometry of the volume conductor, which is variable in different subjects or in the same subject when recordings are made at different gestational ages. The scope of this thesis is to develop a novel methodology for modeling the fetal heart and volume conductor and to use advanced source reconstruction techniques that can reduce the effect of these confounding factors in evaluating heart magnetic signals. Furthermore, we aim to use these new methods for developing a normative database of fMCG metrics at different gestational ages and test their reliability to detect abnormal patterns of cardiac electrophysiology in pregnancies complicated by maternal diabetes. In the first part of the thesis, we review three current fetal heart monitoring modalities, including fetal electrocardiography (ECG), ultrasonography, and fetal magnetocardiography (fMCG). The advantages and drawbacks of each technique are comparatively discussed. Finally, we discuss the developmental changes of fetal heart through gestation as well as the electromagnetic characteristics of the fetal cardiac activation

    Methodological and clinical aspects of ictal and interictal MEG

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    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 menetelmÀ, jolla mitataan aivojen tuottamia heikkoja magneettikenttiÀ. Yksi menetelmÀn tÀrkeimmistÀ kliinisistÀ kÀyttö-tarkoituksista on paikantaa epilepsiapesÀkkeitÀ aivoissa. TÀmÀ on tÀrkeÀÀ epilepsiakirurgian suunnittelussa. Potilaan liikkeet mittauksen aikana ovat aiheuttaneet epÀtarkkuutta pesÀkkeiden paikannukseen ja hÀiriösignaaleja mittauksiin. Ongelma on ollut erityisen korostunut lasten mittauksissa ja epileptisten kohtausten rekisteröinneissÀ. Useimmissa potilaissa MEG-paikannus onkin perustunut kohtausten vÀlisten epileptiformisten aivosÀhköilmiöiden paikannukseen. PitkÀt MEG-rekisteröinnit ovat myös olleet haastavia koska yhteistyökykyisten potilaidenkin on vaikea olla liikkumatta pitkiÀ aikoja. Viime vuosien tekninen kehitys on mahdollistanut MEG-mittaukset myös pÀÀn liikkeiden aikana. Myös aivosignaalien ja kehossa olevien magneettisten materiaalien (esim hammaspaikat, sydÀmen tahdistimet tai aivostimulaattorit) aiheuttamien magneettisten hÀiriöiden erottaminen on nykyisin toteutettavissa. TÀmÀ kehitys on mahdollistanut MEG-mittaukset potilailla, joilla aiemmin ei ollut mahdollisuutta hyötyÀ MEG-paikannuksista ja myös MEG-mittaukset epileptisten kohtausten aikana. TÀrkeÀ osa vÀitöskirjaa on epilepsiakohtausten aikaisten MEG-mittausten kliinisen hyödyn arviointi. Tulokset osoittavat, ettÀ kohtauksenaikaiset MEG-mittaukset paikantavat herkemmin epilepsiakohtauksen lÀhdealueen aivoissa kuin kohtausten vÀlisten epilepsiailmiöiden lÀhdepaikannus. LÀhdealueiden paikannus on yhtÀ tarkka sekÀ aivokuoren pinnalla ettÀ 4 cm syvyydessÀ aivouurteissa. PÀÀ ei kuitenkaan saisi liikkua 3 cm enempÀÀ MEG-mittauksen aikana, ja menetelmÀn herkkyys paranee oilennaisesti magneettikenttien matemaattiseen mallinnukseen perustuvalla magneettisten liikehÀiriöiden poistolla. VÀitöskirja tutkii lisÀksi aivokuoren rakennemuutosten (paikallinen aivokuoridysplasia) aiheuttaman epilepsian kohtausten vÀlisiÀ MEG-mittauksia. PÀinvastoin kuin aiemmin on vÀitetty, ei aina ole tarpeen poistaa koko epileptisia lÀhdealueita sisÀltÀvÀÀ aivojen aluetta hyvÀn leikkaustuloksen saamiseksi. VÀitöskirja esittelee myös laskennallisen MEG-anturiston mÀÀritysmenetelmÀn , joka lisÀÀ MEG-mittausten informaatiosisÀltöÀ huomioimalla pÀÀn liikkeet tulosten analyysissÀ

    Numerical modeling in electro- and magnetoencephalography

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    This Thesis concerns the application of two numerical methods, Boundary Element Method (BEM) and Finite Element Method (FEM) to forward problem solution of bioelectromagnetic source localization in the brain. The aim is to improve the accuracy of the forward problem solution in estimating the electrical activity of the human brain from electric and magnetic field measurements outside the head. Electro- and magnetoencephalography (EEG, MEG) are the most important tools enabling us to gather knowledge about the human brain non-invasively. This task is alternatively named brain mapping. An important step in brain mapping is determining from where the brain signals originate. Using appropriate mathematical models, a localization of the sources of measured signals can be performed. A general motivation of this work was the fact that source localization accuracy can be improved by solving the forward problem with higher accuracy. In BEM studies, accurate representation of model geometry using higher order elements improves the solution of the forward problem. In FEM, complex conductivity information can be incorporated into numerical model. Using Whitney-type finite elements instead of using singular sources such as point dipoles, primary and volume currents are represented as continuous sources. With comparison to analytical solutions available in simple geometries such as sphere, the studied numerical methods show improvements in the forward problem solution of bioelectromagnetic source imaging.reviewe

    Sparse wavelet-based solutions for the M/EEG inverse problem

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    This paper is concerned with variational and Bayesian approaches to neuro-electromagnetic inverse problems (EEG and MEG). The strong indeterminacy of these problems is tackled by introducing sparsity inducing regularization/priors in a transformed domain, namely a spatial wavelet domain. Sparsity in the wavelet domain allows to reach ''data compression'' in the cortical sources domain. Spatial wavelets defined on the mesh graph of the triangulated cortical surface are used, in combination with sparse regression techniques, namely LASSO regression or sparse Bayesian learning, to provide localized and compressed estimates for brain activity from sensor data. Numerical results on simulated and real MEG data are provided, which outline the performances of the proposed approach in terms of localization
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