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

Quantitative evaluation of artifact removal in real magnetoencephalogram signals with blind source separation

The magnetoencephalogram (MEG) is contaminated with undesired signals, which are called artifacts. Some of the most important ones are the cardiac and the ocular artifacts (CA and OA, respectively), and the power line noise (PLN). Blind source separation (BSS) has been used to reduce the influence of the artifacts in the data. There is a plethora of BSS-based artifact removal approaches, but few comparative analyses. In this study, MEG background activity from 26 subjects was processed with five widespread BSS (AMUSE, SOBI, JADE, extended Infomax, and FastICA) and one constrained BSS (cBSS) techniques. Then, the ability of several combinations of BSS algorithm, epoch length, and artifact detection metric to automatically reduce the CA, OA, and PLN were quantified with objective criteria. The results pinpointed to cBSS as a very suitable approach to remove the CA. Additionally, a combination of AMUSE or SOBI and artifact detection metrics based on entropy or power criteria decreased the OA. Finally, the PLN was reduced by means of a spectral metric. These findings confirm the utility of BSS to help in the artifact removal for MEG background activity

Artifact removal in magnetoencephalogram background activity with independent component analysis

The aim of this study was to assess whether independent component analysis (ICA) could be valuable to remove power line noise, cardiac, and ocular artifacts from magnetoencephalogram (MEG) background activity. The MEGs were recorded from 11 subjects with a 148-channel whole-head magnetometer. We used a statistical criterion to estimate the number of independent components. Then, a robust ICA algorithm decomposed the MEG epochs and several methods were applied to detect those artifacts. The whole process had been previously tested on synthetic data. We found that the line noise components could be easily detected by their frequency spectrum. In addition, the ocular artifacts could be identified by their frequency characteristics and scalp topography. Moreover, the cardiac artifact was better recognized by its skewness value than by its kurtosis one. Finally, the MEG signals were compared before and after artifact rejection to evaluate our method

Methods for noninvasive localization of focal epileptic activity with magnetoencephalography

Magnetoencephalography (MEG) is a noninvasive brain signal acquisition technique that provides excellent temporal resolution and a whole-head coverage allowing the spatial mapping of sources. These characteristics make MEG an appropriate technique to localize the epileptogenic zone (EZ) in the preoperative evaluation of refractory epilepsy. Presurgical evaluation with MEG can guide the placement of intracranial EEG (iEEG), the current gold standard in the clinical practice, and even supply sufficient information for a surgical intervention without invasive recordings, reducing invasiveness, discomfort, and cost of the presurgical epilepsy diagnosis. However, MEG signals have low signal-to-noise ratio compared with iEEG and can sometimes be affected by noise that masks or distorts the brain activity. This may prevent the detection of interictal epileptiform discharges (IEDs) and high-frequency oscillations (HFOs), two important biomarkers used in the preoperative evaluation of epilepsy. In this thesis, the reduction of two kinds of interference is aimed to improve the signal-to-noise ratio of MEG signals: metallic artifacts mask the activity of IEDs; and the high-frequency noise, that masks HFO activity. Considering the large number of MEG channels and the long duration of the recordings, reducing noise and marking events manually is a time-consuming task. The algorithms presented in this thesis provide automatic solutions aimed at the reduction of interferences and the detection of HFOs. Firstly, a novel automatic BSS-based algorithm to reduce metallic interference is presented and validated using simulated and real MEG signals. Three methods are tested: AMUSE, a second-order BSS technique; and INFOMAX and FastICA, based on high-order statistics. The automatic detection algorithm exploits the known characteristics of metallic-related interferences. Results indicate that AMUSE performes better when recovering brain activity and allows an effective removal of artifactual components.Secondly, the influence of metallic artifact filtering using the developed algorithm is evaluated in the source localization of IEDs in patients with refractory focal epilepsy. A comparison between the resulting positions of equivalent current dipoles (ECDs) produced by IEDs is performed: without removing metallic interference, rejecting only channels with large metallic artifacts, and after BSS-based reduction. The results show that a significant reduction on dispersion is achieved using the BSS-based reduction procedure, yielding feasible locations of ECDs in contrast to the other approaches. Finally, an algorithm for the automatic detection of epileptic ripples in MEG using beamformer-based virtual sensors is developed. The automatic detection of ripples is performed using a two-stage approach. In the first step, beamforming is applied to the whole head to determine a region of interest. In the second step, the automatic detection of ripples is performed using the time-frequency characteristics of these oscillations. The performance of the algorithm is evaluated using simultaneous intracranial EEG recordings as gold standard.The novel approaches developed in this thesis allow an improved noninvasive detection and localization of interictal epileptic biomarkers, which can help in the delimitation of the epileptogenic zone and guide the placement of intracranial electrodes, or even to determine these areas without additional invasive recordings. As a consequence of this improved detection, and given that interictal biomarkers are much more frequent and easy to record than ictal episodes, the presurgical evaluation process can be more comfortable for the patient and in a more economic way.La magnetoencefalografía (MEG) es una técnica no invasiva de adquisición de señales cerebrales que proporciona una excelente resolución temporal y una cobertura total de la cabeza, permitiendo el mapeo espacial de las fuentes cerebrales. Estas características hacen del MEG una técnica apropiada para localizar la zona epileptogénica (EZ) en la evaluación preoperatoria de la epilepsia refractaria. La evaluación prequirúrgica con MEG puede orientar la colocación del EEG intracraneal (iEEG), el actual modelo de referencia en la práctica clínica, e incluso suministrar información suficiente para una intervención quirúrgica sin registros invasivos; reduciendo la invasividad, la incomodidad y el costo del diagnóstico de la epilepsia prequirúrgica. Sin embargo, las señales MEG tienen baja relación señal ruido en comparación con el iEEG pudiendo imposibilitar la detección de descargas epileptiformes interictales (IEDs) y oscilaciones de alta frecuencia (HFOs), dos importantes biomarcadores utilizados en la evaluación preoperatoria de la epilepsia.En esta tesis, la reducción de dos tipos de interferencia está dirigida a mejorar la relación señal-ruido de la señal MEG: los artefactos metálicos que enmascaran la actividad de las IEDs; y el ruido de alta frecuencia, que enmascara la actividad de las HFOs. Debido al gran número de canales MEG y la larga duración de los registros, tanto reducir el ruido como seleccionar los biomarcadores manualmente es una tarea que consume mucho tiempo. Los algoritmos presentados en esta tesis aportan soluciones automáticas dirigidas a la reducción de interferencias y la detección de HFOs. En primer lugar, se presenta y valida un nuevo algoritmo automático basado en BSS para reducir interferencias metálicas mediante señales simuladas y reales. Se prueban tres métodos: AMUSE, una técnica BSS de segundo orden; y INFOMAX y FastICA, basados en estadísticos de orden superior. El algoritmo de detección automático utiliza las características conocidas de la señal producida por la interferencia metálica. Los resultados indican que AMUSE recupera mejor la actividad cerebral y permite una eliminación efectiva de componentes artefactuales.Posteriormente, se evalúa la influencia del filtrado de artefactos metálicos en la localización de IEDs en pacientes con epilepsia focal refractaria. Se realiza una comparación entre las posiciones resultantes de dipolos de corriente equivalentes (ECDs) producidos por IEDs: sin eliminar interferencias metálicas, rechazando solamente canales con elevados artefactos metálicos y, por último, después de una reducción utilizando el algoritmo BSS desarrollado. Los resultados muestran que se logra una reducción significativa en la dispersión utilizando el procedimiento de reducción basado en BSS, lo que produce ubicaciones factibles de los dipolos en contraste con los otros enfoques.En segundo lugar, se desarrolla un algoritmo para la detección automática ripples epilépticos en MEG utilizando sensores virtuales basados en la técnica de beamformer. La detección de ripples se realiza mediante un enfoque en dos etapas. Primero, se determina el área de interés usando beamformer. Posteriormente, se realiza la detección automática de ripples utilizando las características en tiempo-frecuencia. El rendimiento del algoritmo se evalúa utilizando registros iEEG simultáneos.Los nuevos enfoques desarrollados en esta tesis permiten una detección no invasiva mejor de los biomarcadores interictales, que pueden ayudar a delimitar la zona epileptogénica y guiar la colocación de electrodos intracraneales, o incluso determinar estas áreas sin este tipo de registros. Como consecuencia de esta mejora en la detección, y dado que los biomarcadores interictales son mucho más frecuentes y fáciles de registrar que los episodios ictales, la evaluación prequirúrgica puede ser más cómoda y menos costosa para el paciente.Postprint (published version

Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study

We here compared results achieved by applying popular methods for reducing artifacts in magnetoencephalography (MEG) and electroencephalography (EEG) recordings of the auditory evoked Mismatch Negativity (MMN) responses in healthy adult subjects. We compared the Signal Space Separation (SSS) and temporal SSS (tSSS) methods for reducing noise from external and nearby sources. Our results showed that tSSS reduces the interference level more reliably than plain SSS, particularly for MEG gradiometers, also for healthy subjects not wearing strongly interfering magnetic material. Therefore, tSSS is recommended over SSS. Furthermore, we found that better artifact correction is achieved by applying Independent Component Analysis (ICA) in comparison to Signal Space Projection (SSP). Although SSP reduces the baseline noise level more than ICA, SSP also significantly reduces the signal-slightly more than it reduces the artifacts interfering with the signal. However, ICA also adds noise, or correction errors, to the wave form when the signal-to-noise ratio (SNR) in the original data is relatively low-in particular to EEG and to MEG magnetometer data. In conclusion, ICA is recommended over SSP, but one should be careful when applying ICA to reduce artifacts on neurophysiological data with relatively low SNR.Peer reviewe

Applications of Blind Source Separation to the Magnetoencephalogram Background Activity in Alzheimer’s Disease

En esta Tesis Doctoral se ha analizado actividad basal de magnetoencefalograma (MEG) de 36 pacientes con la Enfermedad de Alzheimer (Alzheimer’s Disease, AD) y 26 sujetos de control de edad avanzada con técnicas de separación ciega de fuentes (Blind Source Separation, BSS). El objetivo era aplicar los métodos de BSS para ayudar en el análisis e interpretación de este tipo de actividad cerebral, prestando especial atención a la AD. El término BSS denota un conjunto de técnicas útiles para descomponer registros multicanal en las componentes que los dieron lugar. Cuatro diferentes aplicaciones han sido desarrolladas. Los resultados de esta Tesis Doctoral sugieren la utilidad de la BSS para ayudar en el procesado de la actividad basal de MEG y para identificar y caracterizar la AD.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemátic

A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies

Electroencephalography (EEG) and magnetoencephalography (MEG) are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and tangential sources, the two methods are complementary. Over the years, neurophysiological studies have changed considerably: high-density recordings are becoming de rigueur; there is interest in both spontaneous and evoked activity; and sophisticated artifact detection and removal methods are available. Improved head models for source estimation have also increased the precision of the current estimates, particularly for EEG and combined EEG/MEG. Because of their complementarity, more investigators are beginning to perform simultaneous EEG/MEG studies to gain more complete information about neural activity. Given the increase in methodological complexity in EEG/MEG, it is important to gather data that are of high quality and that are as artifact free as possible. Here, we discuss some issues in data acquisition and analysis of EEG and MEG data. Practical considerations for different types of EEG and MEG studies are also discussed

Fast transient networks in spontaneous human brain activity

To provide an effective substrate for cognitive processes, functional brain networks should be able to reorganize and coordinate on a sub-second temporal scale. We used magnetoencephalography recordings of spontaneous activity to characterize whole-brain functional connectivity dynamics at high temporal resolution. Using a novel approach that identifies the points in time at which unique patterns of activity recur, we reveal transient (100–200 ms) brain states with spatial topographies similar to those of well-known resting state networks. By assessing temporal changes in the occurrence of these states, we demonstrate that within-network functional connectivity is underpinned by coordinated neuronal dynamics that fluctuate much more rapidly than has previously been shown. We further evaluate cross-network interactions, and show that anticorrelation between the default mode network and parietal regions of the dorsal attention network is consistent with an inability of the system to transition directly between two transient brain states

Aivovamman vaikutus aivokuoren rytmiseen toimintaan

Mild traumatic brain injuries (mTBI) are common, and while most patients recover well, there is a minority of patients suffering from prolonged symptoms lasting over three months. Pathological processes provoke low-frequency (0.5 - 7 Hz) oscillatory brain activity, measurable with electroencephalography (EEG) and magnetoencephalography (MEG). After mTBI, low frequency activity (LFA) is hypothesized to arise from cortical neurons suffering from de-afferentation after traumatic axonal injury. The natural evolution and prognostic value of low-frequency activity (LFA) measured with MEG, however, is not yet firmly established and reliable biomarkers for cognitive complaints after mTBI are lacking. The aim of this thesis was to examine the occurrence and natural evolution of low frequency activity (LFA) after mild traumatic brain injury (mTBI), and to assess its prognostic value in predicting those with prolonged symptoms. Additionally, we wanted to examine the effect of mTBI to brain oscillatory activity during cognitive tasks and find indicators for altered processing. The existence of LFA in healthy subjects might, however, hamper its’ diagnostic value. Therefore, in Study I we created a reference database of resting-state oscillatory brain activity and observed LFA in only 1,4% of healthy subjects’ MEG recordings. The Study II assessed the occurrence and evolution of LFA in resting-state MEG recordings of mTBI patients. At a single-subject level, 7/26 patients presented aberrant 4–7 Hz (theta) band activity; 3/7 patients with abnormal theta activity were without any detectable lesions in MRI. Of the twelve patients with follow-up measurements, five showed abnormal theta activity in the first recording, but only two in the second measurement, implying the importance of early measurements in clinical settings. The presence of LFA was not, however, correlated with the prevalence of self-reported symptoms. The Study III concentrated on the modulation of oscillatory activity during cognitive tasks, Paced Auditory Serial Addition Test (PASAT) and a vigilance test. Attenuation of cortical activity at alpha band (8 – 14 Hz) during PASAT compared with rest was stronger in patients than in controls (p≤0.05, corrected). Furthermore, the patients presented significant attenuation of oscillatory activity also in the left superior frontal gyrus and right prefrontal cortices which was not detected in controls. Spectral peak amplitudes of areal mean oscillatory activity at the alpha band were negatively correlated with the patients’ neuropsychological performance (p<0.01, uncorrected). Areal alpha frequency modulation during PASAT compared with rest was altered in patients: While the alpha peak frequency increased occipitally and remained stable parietally in controls, it was stable occipitally and decreased parietally in mTBI patients (p=0.012). According to our studies, LFA, especially theta-band oscillatory activity can provide an early objective sign of brain dysfunction after mTBI, and cortical oscillatory activity during a demanding cognitive task (PASAT) is altered after mTBI. Our observations suggest that both aberrant theta-band activity and the altered alpha activity during cognitive tasks may offer clinically relevant indicators of changes in neural processing after mTBI.Aivovamma on aivojen rakenteellinen tai toiminnallinen vaurio, joka syntyy kun päähän kohdistuu voimakas ulkoinen energia, tai äkillinen kiihtyvyys-hidastuvuusvoima (kuten äkkipysäyksissä). Aivovamma aiheuttaa tajunnanhäiriön tai muun neurologisen oireiston, joka voi olla ohimenevä tai pysyvä. Lievät aivovammat ovat yleisiä, ja vaikka suurin osa loukkaantuneista toipuu hyvin, kärsii pieni vähemmistö pitkäaikaisista jälkioireista. Lievä aivovamma ei aina aiheuta todennettavia diagnostisia muutoksia, eikä siitä toipumista ennustavia tekijöitä juuri tunneta. Aivosairaudet, myös aivovammat, muuttavat aivojen sähköistä toimintaa ja aiheuttavat matalataajuista rytmistä toimintaa (0.5 – 7 Hz), joka voidaan tunnistaa aivosähkökäyrän (EEG) tai magnetoenkefalografian (MEG) avulla. Aivovamman jälkeisen hidasaaltotoiminnan ajatellaan johtuvan hermosolujen viejähaarakkeiden vaurion aiheuttamasta hermosolujen poikkeavasta sähköisestä toiminnasta. Koska lievän- keskivaikean aivovamman todentaminen voi olla vaikeaa, tutkimme MEG:n mahdollisuuksia diagnostiikan apuvälineenä. Hidasaaltotoiminnan esiintyminen terveillä henkilöillä voisi vähentää löydöksen diagnostista merkitystä vamman jälkeen. Sen vuoksi selvitimme poikkeavan hidasaaltotoiminnan esiintyvyyttä 139:llä terveellä koehenkilöllä ja havaitsimme poikkeavia hidasaaltoja vain kahdella (1.4%). Lievän aivovamman saaneista 26:sta potilaasta hidasaaltotoimintaa (4-7 Hz) esiintyi 7:llä (27%). Kolmella heistä ei pystytty havaitsemaan poikkeavia muutoksia aivojen rakenteellisessa magneettikuvauksessa. Seurantamittaus kuuden kuukauden kuluttua saatiin tehtyä 12 potilaalle. Heistä viidellä (42%) havaittiin hidasaaltotoimintaa ensimmäisessä mittauksessa, mutta seurantamittauksessa vain kahdella. Aikainen mittausajankohta vamman jälkeen vaikuttaa siten parantavan tutkimuksen herkkyyttä. Alkuvaiheen hidasaaltotoiminta ei vaikuttanut ennustavan mahdollisen jälkioireiston kehittymistä, mutta pieni otoskoko vaikeuttaa löydöksen arvioimista. Tarkkaavaisuuden ja muistin häiriöt ovat tavallisimpia oireita aivovamman jälkeen. Tästä syystä tarkastelimme myös muisti- ja tarkkaavaisuustehtävien vaikutusta potilaiden aivojen rytmiseen toimintaan ja mahdollisten muutosten yhteyttä havaittuihin oireisiin. Havaitsimme haastavan muistitehtävän aikana potilaiden rytmisen toiminnan vaimentuvan lepotilanteeseen verrattuna voimakkaammin ja useammilla alueilla ns. alfa-taajuuskaistalla (8-14 Hz) kuin kontrollihenkilöiden. Rytmisen toiminnan voimakkaampi vaimentuminen potilailla oli yhteydessä parempaan neuropsykologiseen testisuoriutumiseen. Myös alueelliset huipputaajuudet käyttäytyivät eri tavoin kontrollihenkilöillä ja potilailla. Kontrollihenkilöillä tehtävän aikana takaraivolohkon alfa-taajuus nousi päälakilohkon taajuuden pysyessä vakaana verrattuna lepotilaan. Potilailla tehtävän aikana alfataajuus käyttäytyi päinvastoin; takaraivolohkon alfa-taajuus säilyi ennallaan, mutta päälakilohkon huipputaajuus laski verrattuna lepotilaan. Tutkimuksemme perusteella pian vamman jälkeen todettava hidasaaltotoiminta voi osoittaa objektiivisesti aivotoiminnan häiriön. Potilailla aivojen rytminen toiminta vaativan kognitiivisen tehtävän aikana erosi kontrolleista. Havaintojemme perusteella sekä hidasaaltotoiminnan esiintyminen, että rytmisen toiminnan muuntuminen kognitiivisten tehtävien aikana voivat jatkossa tarjota kliinisesti merkityksellisiä välineitä arvioitaessa tiedonkäsittelyn tehottomuutta lievän aivovamman jälkeen. Lisätutkimukset laajemmalla aineistolla havaintojemme vahvistamiseksi ovat tarpeellisia aivovamman diagnostiikan kehittämiseksi