Stepping stone sampling for retrieving artifact-free electroencephalogram during functional magnetic resonance imaging

長崎大学学位論文 学位記番号:博(医)乙第1,734号 学位授与年月日:平成17年2月28

MR-compatible Electrophysiology Recording System for Multimodal Imaging

Simultaneous acquisition of functional magnetic resonance imaging (fMRI) and electrophysiological recordings is an emerging multimodal neuroimaging strategy for studying brain functions. However, the strong magnetic field generated during fMRI greatly degrades the electrophysiological signal quality during simultaneous acquisition. Here, I developed a low powered, miniaturized, system – “ECHO” which delivers a hardware and software solution to overcome the challenges presented by multimodal imaging. The device monitors fluctuations in electromagnetic field during fMRI and synchronizes amplification and sampling of electrophysiological signals to minimize effects of gradient and RF artifacts (electromagnetic artifacts). Furthermore, I introduced a concept of wirelessly transmitting recorded data through the MRI receiver coil. ECHO transmits the data at a frequency visible to the MRI receiver coil, after which the transmitted data is readily separable from the MRI image in the frequency domain. The MR-compatibility of the recorder was evaluated through a series of experiments with a phantom to study its effects on the MRI image quality. To further evaluate the effectiveness of ECHO, I recorded electrocardiogram and local field potential (evoked potential) in live rats during concurrent fMRI acquisition. In summary, ECHO offers a ‘plug and play’ solution to capture artifact-free electrophysiological data without the need of expensive amplifiers or synchronization hardware which require physical connection to the MRI scanner. This device is expected to make multimodal imaging more accessible and be applied for a broad range of fMRI studies in both the research and clinical fields

Encoding of electrophysiology and other signals in MR images.

PURPOSE: To develop a gradient insensitive, generic technique for recording of non-MR signals by use of surplus scanner bandwidth. MATERIALS AND METHODS: Relatively simple battery driven hardware is used to transform one or more signals into radio waves detectable by the MR scanner. Similar to the "magstripe" technique used for encoding of soundtracks in motion pictures, the electrical signals are in this way encoded as artifacts appearing in the MR images or spectra outside the region of interest. The encoded signals are subsequently reconstructed from the signal recorded by the scanner. RESULTS: Electrophysiological (EP) eye and heart muscular recording (electrooculography [EOG] and electrocardiography [ECG]) during fast echo planar imaging (EPI) is demonstrated with an expandable, modular 8-channel prototype implementation. The gradient artifacts that would normally be dominating EOG are largely eliminated. CONCLUSION: The method provides relatively inexpensive sampling with inherent microsecond synchronization and it reduces gradient artifacts in physiological recordings significantly. When oversampling is employed, the method is compatible with all MR reconstruction and postprocessing techniques

Detection of experimental ERP effects in combined EEG-fMRI: evaluating the benefits of interleaved acquisition and Independent Component Analysis

Copyright © 2011 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Clinical Neurophysiology . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Clinical Neurophysiology, 2011 Vol. 122 Issue 2, pp. 267-77 DOI: http://dx.doi.org/10.1016/j.clinph.2010.06.033Objective The present study examined the benefit of rapid alternation of EEG and fMRI (a common strategy for avoiding artifact caused by rapid switching of MRI gradients) for detecting experimental modulations of ERPs in combined EEG–fMRI. The study also assessed the advantages of aiding the extraction of specific ERP components by means of signal decomposition using Independent Component Analysis (ICA). Methods ‘Go–nogo’ task stimuli were presented either during fMRI scanning or in the gaps between fMRI scans, resulting in ‘gradient’ and ‘no-gradient’ ERPs. ‘Go–nogo’ differences in the N2 and P3 components were subjected to conventional ERP analysis, as well as single-trial and reliability analyses. Results Comparable N2 and P3 enhancement on ‘nogo’ trials was found in the ‘gradient’ and ‘no-gradient’ ERPs. ICA-based signal decomposition resulted in better validity (as indicated by topography), greater stability and lower measurement error of the predicted ERP effects. Conclusions While there was little or no benefit of acquiring ERPs in the gaps between fMRI scans, ICA decomposition did improve the detection of experimental ERP modulations. Significance Simultaneous and continuous EEG–fMRI acquisition is preferable to interleaved protocols. ICA-based decomposition is useful not only for artifact cancellation, but also for the extraction of specific ERP components

Analysis of electroencephalography signals collected in a magnetic resonance environment: characterisation of the ballistocardiographic artefact

L’acquisizione simultanea di segnali elettroencefalografici (EEG) e immagini di risonanza magnetica funzionale (fMRI) permette di investigare attivazioni cerebrali in modo non invasivo. La presenza del campo magnetico altera però in modo non trascurabile la qualità dei segnali EEG acquisiti. In particolare due artefatti sono stati individuati: l’artefatto da gradiente e l’artefatto da ballistocardiogramma (BCG). L’artefatto da BCG è legato all’attività cardiaca del soggetto, ed è caratterizzato da elevata variabilità tra un’occorrenza e l’altra in termini di ampiezza, forma d’onda e durata dell’artefatto. Differenti algoritmi sono stati implementati al fine di rimuoverlo, ma la rimozione completa rimane ancora un difficile obiettivo da raggiungere a causa della sua complessa natura. L’argomento della tesi riguarda l’analisi di segnali EEG acquisiti in ambiente di risonanza magnetica e la caratterizzazione dell’artefatto BCG. L’obiettivo è individuare ulteriori caratteristiche dell’artefatto che possano condurre al miglioramento dei precedenti metodi, o all’implementazione di nuovi. Con questa tesi abbiamo mostrato quali sono i motivi che causano la presenza di residui artefattuali nei segnali EEG processati con i metodi presenti in letteratura. Attraverso analisi statistica abbiamo riscontrato che occorrenze dell’artefatto BCG sono caratterizzate da un ritardo variabile rispetto al picco R sull’ECG, che nella nostra analisi rappresenta l’evento di riferimento nell’attività cardiaca. Abbiamo inoltre trovato che il ritardo R-BCG varia con la frequenza cardiaca. Le successive valutazioni riguardano i maggiori contributi all’artefatto BCG. Attraverso l’analisi alle componenti principali, sono stati individuati due contributi legati al fluire del sangue dal cuore verso il cervello e alla sua pulsatilità nei vasi principali dello scalpo

Ballistocardiogram artifact removal from EEG signal

Diplomová práce se zabývá hybridním vyšetřením fMRI-EEG. V EEG signálu jsou přítomny dva základní artefakty, gradientní a balistokardiografický. Po odstranění gradientního artefaktu a detekce R vln v kanálu EKG byl navržen postup pro odstranění balistokardiografického artefaktu, pomocí metody odečtu artefaktového vzoru, pomocí metody ICA (metody nezávislých proměnných) a metody spojující obě uvedené. Byla vyhodnocena úspěšnost všech metod a jejich vliv na evokované potenciály v signálu EEG.Master´s thesis deals with hybrid examination fMRI-EEG. Two main artifacts are present in EEG, imaging (gradient) and ballistocardiographic. After gradient artifact removal and R wave detection in ECG channel, approach for ballistographic artifact removal was proposed, with help of artifact template subtraction method, ICA and combined method. Efficiency and influence on evoked potentials of all methods were evaluated.

Multimodal Integration: fMRI, MRI, EEG, MEG

This chapter provides a comprehensive survey of the motivations, assumptions and pitfalls associated with combining signals such as fMRI with EEG or MEG. Our initial focus in the chapter concerns mathematical approaches for solving the localization problem in EEG and MEG. Next we document the most recent and promising ways in which these signals can be combined with fMRI. Specically, we look at correlative analysis, decomposition techniques, equivalent dipole tting, distributed sources modeling, beamforming, and Bayesian methods. Due to difculties in assessing ground truth of a combined signal in any realistic experiment difculty further confounded by lack of accurate biophysical models of BOLD signal we are cautious to be optimistic about multimodal integration. Nonetheless, as we highlight and explore the technical and methodological difculties of fusing heterogeneous signals, it seems likely that correct fusion of multimodal data will allow previously inaccessible spatiotemporal structures to be visualized and formalized and thus eventually become a useful tool in brain imaging research