Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI

Simultaneous EEG-fMRI allows multi-parametric characterisation of brain function, in principle enabling a more complete understanding of brain responses; unfortunately the hostile MRI environment severely reduces EEG data quality. Simply eliminating data segments containing gross motion artefacts [MAs] (generated by movement of the EEG system and head in the MRI scanner’s static magnetic field) was previously believed sufficient. However recently the importance of removal of all MAs has been highlighted and new methods developed.A systematic comparison of the ability to remove MAs and retain underlying neuronal activity using different methods of MA detection and post-processing algorithms is needed to guide the neuroscience community. Using a head phantom, we recorded MAs while simultaneously monitoring the motion using three different approaches: Reference Layer Artefact Subtraction (RLAS), Moire Phase Tracker (MPT) markers, and Wire Loop Motion Sensors (WLMS). These EEG recordings were combined with EEG responses to simple visual tasks acquired on a subject outside the MRI environment. MAs were then corrected using the motion information collected with each of the methods combined with different analysis pipelines.All tested methods retained the neuronal signal. However, often the MA was not removed sufficiently to allow accurate detection of the underlying neuronal signal. We show that the MA is best corrected using the RLAS combined with post-processing using a multi-channel, recursive least squares (M-RLS) algorithm. This method needs to be developed further to enable practical utility; thus, WLMS combined with M-RLS currently provides the best compromise between EEG data quality and practicalities of motion detection

Mukautuvan häiriönpoistoalgoritmin kehitys reaaliaikaisia aivosähkökäyrämittauksia varten

Työn tavoitteena oli kehittää algoritmi aivosähkökäyrän häiriöiden reaaliaikaiseen poistamiseen. Työ oli osa uuden laitteen kehitysprojektia, jossa pyritään vähentämään tietyntyyppisiin aivosähkökäyrämittauksiin kuluvaa aikaa ja helpottamaan mittausten suorittamista. Mittaukset tehtiin laitteen kahdeksankanavaisella prototyypillä. Artefaktojen ominaispiirteet määritettiin kokeellisesti. Tärkeimmiksi häiriölähteiksi todettiin silmien räpäytykset, silmien liikkeet, pään liikuttaminen sekä purenta. Ensisijaisesti häiriöiden tunnistamisessa käytettiin laskennallisesti kevyitä virtuaalikanavamenetelmiä, jotka hyödynsivät havaittuja piirteitä. Menetelmiä kehitettiin edelleen useiden koemittausten avulla. Myöhemmissä versioissa algoritmi saatiin mukautumaan erilaisiin mittaustilanteisiin ja muutoksiin mittauksen kuluessa. Lopullinen algoritmi on huomattavasti tehokkaampi ja luotettavampi kuin aiemmin käytetyt reaaliaikaiset menetelmät. Aiemmat menetelmät ovat perustuneet yksittäiseen raja-arvoon ja niiden hylkäysprosentit ovat korkeintaan 80% käytettäessä samoja kriteereitä kuin tässä työssä. Viimeisimmissä suorituskykykokeissa algoritmi tunnisti ja hylkäsi noin 99% artefaktoista ja hylkäyksistä yli 98% oli oikeaan osuneita. Kokeessa käytettiin useita koehenkilöitä ja mittaustilanne oli mahdollisimman tarkasti laitteen todellista käyttötilannetta jäljittelevä. Tämä osoittaa, että algoritmi on erittäin tehokas ja pystyy mukautumaan sopivaksi kullekin koehenkilölle normaaleissa mittaustilanteissa. Lopullisessa muodossaan kahdeksankanavainen algoritmi soveltuu mainiosti projektissa kehitettävän laitteen häiriönpoistoalgoritmiksi. Se on tehokas, luotettava ja laskennallisesti verraten kevyt. Mikäli laitteesta kehitetään jatkossa versio, jossa häiriönpoisto tapahtuu sulautetulla prosessorilla, on kehitetty algoritmi varteenotettava ehdokas toteutukseksi. Myös muunlaiset aivosähkökäyrälaitteet ovat potentiaalisia sovelluskohteita algoritmille, sillä häiriönpoisto on eräs niiden yleisimmistä heikkouksista.The purpose of the work was to develop an online algorithm for electroencephalograph (EEG) artefact removal. The work was part of a project developing a novel device for easier and faster recording of event related potentials (ERPs). A prototype of the device was used in the recordings involved in the development of the algorithm. The properties of the artefacts were studied experimentally. Most important artefact sources turned out to be blinks, eye movements, head movements, and jaw muscle activations. The primary methods used in artefact detection were several virtual channel methods that are computationally light and take advantage of the experimentally determined properties. Several developments were made to the methods with the aid of further experimental data. In later versions adaptive features were introduced to the algorithm, allowing it to adjust to changes in measurement conditions without outside interruption. The final version of the algorithm is more powerful and robust than other online solutions. Earlier solutions have relied on a single potential threshold and have reached only 80% accuracy at best when assessed using the same criteria as the algorithm presented here. In the latest performance tests the algorithm detected and rejected approximately 99% of all artefacts, with over 98% of the rejections being correct. Several test subjects were used in the tests and the recording set-up closely mimicked the set-up where such a device would be used in reality. The tests prove that the algorithm is very powerful and can adapt to different subjects under ordinary but not necessarily identical conditions. In the final version presented in this work the eight channel algorithm is well suited to remove the artefacts present in the data measured by the device. It is powerful, reliable, and efficient compared to the alternatives. If the device is developed to include an embedded processor for artefact rejection the algorithm is a good candidate for implementation. The algorithm could also be of use in other EEG applications after some minor modifications, because artefact detection is one of the most common weaknesses of the devices

Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression

Combining electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) offers the potential for imaging brain activity with high spatial and temporal resolution. This potential remains limited by the significant ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation-related head movement within the magnetic field. We model the BCG artifact using a harmonic basis, pose the artifact removal problem as a local harmonic regression analysis, and develop an efficient maximum likelihood algorithm to estimate and remove BCG artifacts. Our analysis paradigm accounts for time-frequency overlap between the BCG artifacts and neurophysiologic EEG signals, and tracks the spatiotemporal variations in both the artifact and the signal. We evaluate performance on: simulated oscillatory and evoked responses constructed with realistic artifacts; actual anesthesia-induced oscillatory recordings; and actual visual evoked potential recordings. In each case, the local harmonic regression analysis effectively removes the BCG artifacts, and recovers the neurophysiologic EEG signals. We further show that our algorithm outperforms commonly used reference-based and component analysis techniques, particularly in low SNR conditions, the presence of significant time-frequency overlap between the artifact and the signal, and/or large spatiotemporal variations in the BCG. Because our algorithm does not require reference signals and has low computational complexity, it offers a practical tool for removing BCG artifacts from EEG data recorded in combination with fMRI.National Institutes of Health (U.S.) (Award DP1-OD003646)National Institutes of Health (U.S.) (Award TR01-GM104948)National Institutes of Health (U.S.) (Grant R44NS071988)National Institute of Neurological Diseases and Stroke (U.S.) (Grant Grant R44NS071988

Multimodal functional neuroimaging: new insights from novel head modeling methodologies

2009/2010Neuroimaging plays a critically important role in neuroscience research and management of neurological and mental disorders. Modern neuroimaging techniques rely on various “source” signals that change across different spatial and temporal scales in accompany with neuronal activity. Nowadays, several types of noninvasive neuroimaging modalities are available based on biophysical signals related to either brain electrophysiology or hemodynamics/metabolism. In this dissertation, advanced model-based neuroimaging methods for the estimation of cortical brain activity from combined high-resolution electroencephalography (EEG), multimodal Magnetic Resonance Imaging (MRI) and functional Magnetic Resonance Imaging (fMRI) data are presented. The present dissertation begins with a review of the current state-of-the-art in the major neuroimaging techniques. Particular attention has been devoted to EEG modelling since such signals propagate (virtually) instantaneously from the activated neuronal tissues via volume conduction to the recording sites on/above the scalp surface. The instantaneous nature of EEG indicates an intrinsically high temporal resolution and precision, which make it well suited for studying brain functions on the neuronal time scale. The collective nature suggests low spatial resolution and specificity, which impede mapping brain functions in great regional details. However, this is regardless of recent advancements in electromagnetic source imaging, which has led to great strides in improving the EEG/MEG spatial resolution to a centimetre scale or even smaller. These methods entail: 1) modeling the brain electrical activity; 2) modeling the head volume conduction process so as to link the modeled electrical activity to EEG; and 3) reconstructing the brain electrical activity from recorded EEG data. For this aim, a subject's multicompartment head model (scalp, skull, CSF, brain cortex, white matter) is constructed from either individual magnetic resonance images or approximated geometry models. We compared different spherical and realistic head modelling techniques in estimating EEG forward solutions from current dipole sources distributed on a standard cortical space reconstructed from Montreal Neurological Institute (MNI) MRI data. Computer simulations are presented for three different four-shell head models, two with realistic geometry, either surface-based (BEM) or volume-based (FDM), and the corresponding sensor-fitted spherical-shaped model. Point Spread Function (PSF) and Lead Field (LF) cross-correlation analyses were performed for 26 symmetric dipole sources to quantitatively assess models’ accuracy in EEG source reconstruction. Both statistical and imaging analysis point to the realistic geometry as a relevant factor of improvement, particularly important when considering sources placed in the temporal or in the occipital cortex. In these situations, using a realistic head model will allow a better spatial discrimination of neural sources when compared to the spherical model. Moreover a brief overview of Diffusion Weighted Imaging and Diffusion Tensor Imaging is also given, as their application in modelling refinement is increasing the accuracy and the complexity of the brain models. Both fMRI and EEG represent brain activity in terms of a reliable anatomical localization and a detailed temporal evolution of neural signals. Simultaneous EEG-fMRI recordings offer the possibility to greatly enrich the significance and the interpretation of the single modality results because the same neural processes are observed from the same brain at the same time. Nonetheless, the different physical nature of the measured signals by the two techniques renders the coupling not always straightforward, especially in cognitive experiments where spatially localized and distributed effects coexist and evolve temporally at different temporal scales. The purpose of the last chapter is to illustrate the combination of simultaneously recorded EEG and fMRI signals exploiting the principles of EEG distributed source modelling. We define a common source space for fMRI and EEG signal projection and novel framework for the spatial and temporal comparative analysis. We use simultaneous EEG-fMRI in order to explore the relationship between the envelope of spontaneous neuronal oscillations in the alpha frequency band (8-13 Hz) recorded with EEG during eyes closed rest and spontaneous fluctuations of the fMRI BOLD signal. We showed on a single-subject analysis how the presented approach, when combined to an accurate realistic head modelling, is able to localize the alpha rhythmic modulation in the occipital visual area and the parieto-occipital sulcus. This finding is in line with recent studies, asserting that, within these regions, time-frequency analysis and phase-synchronization analysis indicated increased alpha power and alpha-band phase-synchronization in eyes-closed condition versus eyes-open condition. Given the lack in the scientific literature of group-analysis experimental studies performed with realistic modelling approach in this field, this topic will be further investigated in future work.XXII Ciclo198

Imaging functional and structural networks in the human epileptic brain

Epileptic activity in the brain arises from dysfunctional neuronal networks involving cortical and subcortical grey matter as well as their connections via white matter fibres. Physiological brain networks can be affected by the structural abnormalities causing the epileptic activity, or by the epileptic activity itself. A better knowledge of physiological and pathological brain networks in patients with epilepsy is critical for a better understanding the patterns of seizure generation, propagation and termination as well as the alteration of physiological brain networks by a chronic neurological disorder. Moreover, the identification of pathological and physiological networks in an individual subject is critical for the planning of epilepsy surgery aiming at resection or at least interruption of the epileptic network while sparing physiological networks which have potentially been remodelled by the disease. This work describes the combination of neuroimaging methods to study the functional epileptic networks in the brain, structural connectivity changes of the motor networks in patients with localisation-related or generalised epilepsy and finally structural connectivity of the epileptic network. The combination between EEG source imaging and simultaneous EEG-fMRI recordings allowed to distinguish between regions of onset and propagation of interictal epileptic activity and to better map the epileptic network using the continuous activity of the epileptic source. These results are complemented by the first recordings of simultaneous intracranial EEG and fMRI in human. This whole-brain imaging technique revealed regional as well as distant haemodynamic changes related to very focal epileptic activity. The combination of fMRI and DTI tractography showed subtle changes in the structural connectivity of patients with Juvenile Myoclonic Epilepsy, a form of idiopathic generalised epilepsy. Finally, a combination of intracranial EEG and tractography was used to explore the structural connectivity of epileptic networks. Clinical relevance, methodological issues and future perspectives are discussed