Application of Magnetoencephalography and spectro-temporal analysis methods to the study of "real life" auditory scenes perception

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Introduction

In everyday life situations, several sound sources combine to form a complex auditory scene. Although all of these sounds reach our ears as a mixture, our auditory system is able to reconstruct the original sound sources and perceive them as separate auditory objects, the mental representations of the auditory sources. This process of recovering individual sources from complex auditory scenes is called auditory scene analysis ([1]) and is the basis of our ability to form and process meaningful auditory perceptions. Understanding the mechanisms underlying the auditory scene analysis is an important step in designing devices to restore auditory perception to those who have lost some or all of their hearing abilities.

So far, neuroimaging studies have led to relevant knowledge about cortical spatio-temporal representation of elementary acoustic features, such as frequency, loudness, modulation and bandwidth. Nevertheless, this studies focused mainly on investigation of processing of simple synthetic sounds, rather than complex auditory scenes. There-fore, little is known about how brain utilizes these elementary features in order to form discrete auditory perceptions of mixed sound sources.

An important topic in auditory perception is attention. In fact, the human brain is not only able to segregate an acoustic mixture in discrete auditory objects, but also to focus listener's attention on a specic sound in the mixture. This ability is referred to as the cocktail party eect ([2]), with reference to the common situation in which we are having a conversation in a noisy environment, focusing our attention on the talker and ignoring all background noises. Until now, neuroscientic research on the perception of speech has focused on the already complex problem of recognizing the speech of an isolated talker ([3]). In these studies, speech is presented without any competing sounds, thus the problem of how we follow the speech of a particular talker in a mixture of sounds is ignored. Nevertheless, understanding the neural processes underlying the cocktail party eect can have signicant practical and theoretical implications. For example, it could lead to an improvement of the speech perception in noisy environments by users of cochlear implants, which is currently very poor.

The aim of this study is to investigate the brain's behavior during a typical cocktail party situation. To this purpose, complex auditory scenes composed of a mixture of human voice and natural environmental sounds will be used for the stimulation; as experimental task, subjects will be asked to attend the voice or the background noise. Magnetoencephalography (MEG, [4]) will be used for monitoring the brain activity during the task. MEG is a non-invasive brain imaging technique that measures the

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CONTENTS magnetic elds produced by electrical activity of a population of neurons in the brain. Among the various kinds of functional neuroimaging methodologies available today, the major advantages of MEG are its ability to provide ne time resolution of the millisecond order, and the fact that it reects the dynamics of neural activity directly, rather than, for example, changes in blood ow or metabolism (e.g., functional magnetic resonance imaging -fMRI-, and positron emission tomography -PET-). These are the main reasons why MEG has been chosen for this study.

Chapter 1 describes the generation of neuromagnetic elds, how MEG can be used to record them, and which are the strategies of MEG data analysis. Finally, it recounts dierent approaches for estimating activity at the cortical level from data recorded by MEG sensors.

Chapter 2 details the experimental procedures used for the acquisition of MEG signals, the signal processing applied in order to obtain artifact-free data, and the realignment of the artifact-free signals to a target sensor array.

Chapter 3 reports the time-domain sensor-level analysis of MEG data, the estimate of neural sources underlying the averaged signals and the results of the statistical analysis of dierential eects at the source level.

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Chapter 1 Magnetoencephalography

Magnetoencephalography (MEG, [4]) is a non-invasive brain imaging technique con-cerned with detection and interpretation of magnetic elds produced by the electrical activity of a population of neurons in the human brain. Thus, MEG reects the dy-namics of neural activity directly, rather than, for example, changes in blood ow or metabolism. The time resolution of MEG is in the millisecond range, orders of magni-tude better than in functional magnetic resonance imaging (fMRI); therefore, together with electroencephalography (EEG), MEG is the only functional imaging technique that allows us to follow the neural processes at the level of cell populations. Unfortu-nately, the spatial resolution is limited because of the ill-posed nature of the inverse problem that must be solved in order to estimate the sources of activity from MEG data.

In this chapter the neuronal basis of MEG, the theory of the method and an overview of the algorithms for the analysis of MEG signals is reported.

1.1 Generation of neuromagnetic elds

Neural activity in the brain gives rise to various kinds of potentials at the cellular level, most notably action potentials and postsynaptic potentials. The associated currents spread in the surrounding volume conductor and alter the magnetic eld in the vicinity of the head, yielding the signal that is recorded by MEG. Although action potentials are the strongest potentials generated by neural activity, the main sources of the magnetic elds measured by MEG are ionic currents in the cortical pyramidal cells generated by postsynaptic potentials [5]. There are basically two reasons for that: rst, the postsynaptic potential can be described by a current dipole, while the action potential by a quadrupole, whose magnetic eld decays more rapidly than that of a dipole; second, the duration of action potentials is in the range of 1 ms, thus an high level of synchronization among neurons is required in order to have a detectable signal. On the other side, postsynaptic currents last tens of milliseconds; this means that even without a perfect synchronization, they can sum in a more eective way and give rise to signals detectable outside the head.

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Chapter 1. Magnetoencephalography The postsynaptic potential is associated with two kind of currents: the primary cur-rent and the volume curcur-rent. The primary curcur-rent can be considered to be the result of neural activity, it is related to the movement of ions due to their chemical con-centrations gradients and it ows mainly inside or in the vicinity of the postsynaptic cell. Thus, by nding the primary current we can locate the site of the active brain region. By contrast, the volume current component is a passive ohmic current, that simply completes the loop of ionic ow so that there is no buildup of charge, and ows in the surrounding medium with a distribution depending on the conductivity prole. Although both primary and volume currents generate associated magnetic elds, in an approximately spherical conductor such as the head, the magnetic elds from the volume currents tend to cancel each other out, rendering MEG most sensitive to the elds from the desired primary currents [4]. Moreover, experience has shown that the normal component of magnetic eld (the one perpendicular to the scalp), is indeed not very sensitive to volume currents; this is one of the reason why MEG measurements favour the normal component of the magnetic eld [5].

For the magnetic elds to be measurable at a distance from the source, it is important that the underlying neuronal currents are well organized both in space and time: the dendrites must be aligned in parallel and the synaptic activations must occur in syn-chrony. For this reasons, macrocolumns of tens of thousands of synchronously activated large pyramidal cortical neurons are believed to be the main MEG generators because of the distribution of their dendrites that are lined up perpendicularly to the cortical surface [6].

The primary current neural source can be modeled as a current dipole. Usually, the current-dipole moments required to explain the measured magnetic eld strengths out-side the head are on the order of 10 nAm [4]. Therefore, about a million synapses must be simultaneously active during a typical evoked response. Since there are approxi-mately 105 _{pyramidal cells per mm}2 _{of cortex and thousands of synapses per neuron,}

each accounting for about 20 fAm, the simultaneous activation of as one synapse in a thousand over an area of one square millimeter would suce to produce a detectable signal. In practice, activation of larger areas is necessary because there is partial can-cellation of the generated magnetic elds owing to source currents owing in opposite directions in neighbouring cortical regions.

Some of the main criticisms of MEG have to do with its spatial sensitivity to neuronal currents in the brain and the existence of silent sources. There are two well-known examples of silent sources: radial primary currents and deep sources [5]. If we assume the head to be an approximately spherical conductor, then for cortical generators that are aligned in a perfectly radial pattern, the volume current causes an equal but oppo-site eld to that generated by the primary current. Then perfect cancellation occurs and the net external eld is zero (Figure 1.1 b and d). Actually, since the human head does not exhibit a spherical symmetry, primary currents with an approximately radial direction are not completely silent. However, this does not alter the fact that the observed magnetic eld is predominantly caused by currents oriented orthogonal to the radial direction, called tangential currents (Figure 1.1 b and c) . As a conse-quence, MEG measures mainly activity from the ssures of the cortex and fortunately

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Chapter 1. Magnetoencephalography

Figure 1.1: Origin of MEG signal. (a) Coronal section of the human brain. (b) Schemati-zation of tangential and radial sources. The head is approximated by a spherical conductive volume. (c) Tangential currents produce detectable magnetic elds. (d) Radial currents do not produce magnetic elds outside the head. [7]

all primary sensory areas of the brain (auditory, somatosensory and visual) are located within ssures [4].

With regard to deep sources, they results to be silent in the case of a spherical volume conductor because in such a symmetry the amplitude of the MEG signal does not only decrease with the squared distance between source and measurement site, but is also proportional to the distance between source and the centre of the sphere [5]. Thus, a source in the centre of the sphere does not generate any external measurable magnetic eld. In practice, a deep source is not totally silent, but produce a magnetic eld which is about one order of magnitude smaller than that of a comparable supercial source. Several studies, however, suggest that MEG can detect cortical activity from relatively deep structures such as the hippocampus or amygdala, provided sucient signal is available compared to the noise in the data [9].

1.2 Measurement of neuromagnetic elds

Magnetic elds produced by neural currents are extraordinarily weak, on the order of several tens of femtoTeslas ([6]), thus their measurement is technically challenging and requires sophisticated sensing technology.

Modern MEG systems use whole-head sensor arrays for measuring the neuromagnetic eld at multiple locations, typically from 100 to 300. The core of all modern MEG sys-tems is the superconducting quantum interference device (SQUID, [10, 11]). A SQUID can be modeled as a small (2-3 mm) ring of superconducting material interrupted by one or two insulating breaks, known as Josephson junctions. When the SQUID is immersed in liquid helium, it becomes superconducting and a quantum mechanical tunneling current can ow across the junctions. The current is modulated in a very sensitive fashion by the external magnetic eld threading the loop. This makes the

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Figure 1.2: Comparison of biomagnetic and unshielded environmental elds[8].

SQUID the most sensitive magnetic eld detector known, and the only one that oers sucient sensitivity for the measurement of neuromagnetic elds [4].

The SQUID itself has a very small area, so it is usually coupled to larger pick up coils that collect the magnetic eld and, by means of inductive coupling, funnel the measured ux into the SQUID ring.

Although the high sensitivity of the SQUIDs coupled to pick up coils, a noise rejection strategy is necessary in order to avoid the signal being overwhelmed by electromagnetic noise from environment and physiological sources. A comparison of typical magnitudes of the environmental noise and biomagnetic elds is shown in Figure 1.2.

The rst defence against noise is obtained by placing the MEG system in a shielded room. In addition to that, instead of a single pick-up coil, a combination of magne-tometers is used in order to have a gradiometer as primary sensor and thus making the SQUID sensitive to an approximation of the spatial gradient rather than to the eld itself. This noise rejection strategy relies on the fact that the gradients of the magnetic eld decay more rapidly than the eld itself, thus the gradients due to dis-tant sources are reduced far more than the elds, while for the near (brain) sources the magnetometers and gradiometers have comparable sensitivities. The higher is the gradient order, the better is the attenuation of distance sources: by using increasingly complex combinations of coils it is possible to make rst- second- and third-order gra-diometers sensitive to higher-order spatial gradients (Figure 1.3). However, hardware gradiometers are dicult to manufacture accurately, thus in practice MEG system only use magnetometers or rst-order gradiometers as primary sensors and software higher

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Figure 1.3: Examples of axial magnetometers\gradiometers and planar gradiometers for bio-magnetic applications. Under each device, the response to a tangential dipolar current is shown. Solid and dashed lines indicate dierent eld polarities. Note how, with the axial devices, the contour maps become spatially tighter for higher-order gradiometers. One ad-vantage of the planar gradiometer is that it gives a maximum detection signal directly above the source. [9]

order gradiometers are implemented for eective noise reduction [7].

1.3 Interpretation of neuromagnetic elds: Evoked

and Induced eects

Historically, MEG data analysis has relied on the averaged evoked response paradigm in order to increase the SNR of MEG signal, otherwise too low for successful source reconstruction. This method consists in performing stimulus-lock averaging of many (about 100) individual responses to a given stimulus. In this way the part of the recorded signal that exhibits only a small variability among dierent epochs, so-called evoked response, is identied, while all signal components that are not phase-locked to the stimulus are averaged out, thus the SNR is enhanced (Figure 1.4 A -blue boxes-, B and C). The underlying assumption of this method is that the activation is both time- and phase-locked to the stimulus; this is however not always the case. The requirement of phase-locking is in fact well satised in primary sensory and motor ar-eas which activate synchronously to external stimuli, wherar-eas responses to cognitive tasks have much more variable latency and this make them impossible to be identi-ed by averaging techniques, which tend to cancel them out (Figure 1.5). Moreover, the evoked paradigm is not suited to the study of induced changes in the power of the ongoing cortical oscillations that are time-locked but not necessarily phase-locked,

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Figure 1.4: (A) Simulated MEG data with a phase locked response (blue boxes) and an induced one (green boxes). Note that in the single trials the shape of the responses is covered by noise. (B) Averaging across trials gives the conventionale evoked eld. (C) Spectrogram of the evoked response. (D) Spectrogram of single trials. (E) Average spectrogram across trials. In all time-frequency representations the power changes with respect to a pre-stimulus baseline are shown. [13]

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Chapter 1. Magnetoencephalography so-called event-related synchronization and desynchronization (ERS/ERD), that have been demonstrated to play an important role in a variety of dierent sensory-motor and cognitive tasks. For example, it has been demonstrated that the alpha rhythm (8-13 Hz) in the visual cortex increases in power when a subject shut his eyes [14]. In order to detect this induced phenomena and study cognitive functions with MEG, it is necessary an analysis method that does not rely on average evoked responses. The method that is commonly used applies the time-frequency decomposition to each trial and averages the ensuing power across trials (Figure 1.4 A -green boxes-, D and E). The resulting spectrogram will include the power of both evoked and induced activity, as long as the SNR is high enough and the jitter across trials does not exceed the duration of the window used for the time-frequency analysis.

1.4 Forward and inverse problems

Given the position, magnitude and orientation of an hypothetical current source in the brain, together with a suciently realistic volume conductor model, the magnetic eld that would be measured in any given MEG detector array can be calculated from Maxwell's equations. This is known as the MEG forward problem and, because there is only one possible magnetic eld distribution that can be generated by a given cur-rent conguration, it has a unique solution. The basic problem in MEG is just the other way round: how to estimate and localize the cortical sources corresponding to a certain distribution of magnetic elds recorded outside the head. This is known as the MEG inverse problem and, unfortunately, it is an ill-posed problem that has no unique solution: because there are an innite number of cortical current distributions that could generate any given externally measured magnetic eld, the solution of the MEG inverse problem is impossible from the measured data alone. There is insucient information in the external eld measurements to tell us the distribution of electrical currents in the brain and this holds even true for the hypothetical case that the mag-netic eld resulting from the brain activities of interest could be measured without noise. Therefore, one must assume specic models of the source and of the volume conductor in order to estimate approximate solutions.

Nonuniqueness of the inverse problem has serious implications for the utility of MEG as functional imaging techniques; however, if we can add any a priori constraints about the spatial or temporal nature of the cortical currents (based on the known anatomy and physiology of the brain), then we can often collapse the space of possible solutions to a single one, which is unique in that context. In this way, physiologically meaningful solutions of MEG patterns can be found. Of course, if our a priori information is wrong then our solution will be wrong. This illustrates an important point: solutions to the inverse problem stand or fall on the validity of their a priori constraints.

Several source reconstruction algorithms, each employing a dierent set of assumption, have been developed in order to overcome the ill-posed inverse problem. They can be roughly subdivided into parametric methods, usually assuming a source consisting of a few current dipoles, and imaging (or tomographic) methods, based on the idea of

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Figure 1.5: Eect of the latency jitter in the average response amplitude. The response obtained from averaging 100 simulated, noise-free epochs, with latency jitter taken from a normal distribution. The insert shows the original epoch and the jittered epochs.[14]

a continuous distribution of primary currents in the brain [6]. Parametric methods include equivalent current dipole (ECD, [18]) tting techniques, while beamformers ([20]) and the minimum-norm estimate (MNE, [15, 16, 17]) belong to the class of imaging methods.

1.4.1 Equivalent current dipole

The equivalent current dipole is the oldest model for brain source activity. It assumes that an active area in the brain can be modeled as a point source of current, known as an equivalent current dipole. Although we know that the active area has to be at least a few square millimeters in size in order to generate a detectable signal, it is far enough from the detectors to be accurately modeled as a point source of current. It is important to note that such a model does not mean that somewhere in the brain there exists a discrete dipolar source, but rather that the dipole is a convenient representation for coherent activation of a large number of pyramidal cells, extending over a few square centimeters of gray matter. The ECD analysis proceeds by guessing the number of dipoles and their initial positions and determining the dipole parameters by a non-linear search that minimize dierences between the eld computed from the dipole model and that which is measured. The estimation of equivalent dipole model is only meaningful if the scalp eld has a dipolar shape and the number of possible active areas can be anticipated with reasonably accuracy (Figure 1.6).

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Figure 1.6: Example of typical two-dipole eld pattern.

1.4.2 Minimum-norm estimates

The minimum-norm estimate does not impose explicit constraints on the current distri-bution but assume that activity can occur anywhere within the brain, thus no a priori information about number and position of the sources are required. The whole source space is lled with a dense eld of xed dipoles and the amplitudes of the dipoles at each point is determined by nding, among all possible current distributions which explain the measured eld, the most probable one in the sense of the minimum norm of the currents. One problem with this approach is that the source conguration sat-isfying the minimum norm constraint will always exist at the most supercial layer of the source space and hence some depth weighting factor must be introduced in the calculations.

Minimum current estimation (MCE) is a subclass of minimum norm estimates. In MNE, the minimized norm for the current is the L2-norm (absolute value squared), whereas in MCE the norm used is L1 (absolute value). The L1 norm results in more focal source estimates than L2 norm, which might be neurophysiologically more feasible. Because cortical currents are estimated with minor user interaction, MNE could be a more objective method than dipole modeling [19]; moreover, the distributed MNE-based source activations often look more physiological than the point-like current dipoles. However, a word of caution is appropriate here because the appearance of the result depends strongly on the method used; the MNE approach gives a distribute solution and the dipole approach a local solution, whatever the real current distribution is. Thus the validity of the solution depends strongly on the validity of the assumptions about the source conguration.

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1.4.3 Beamforming

Beamformig is a localization method based on the principles of spatial ltering. In beamforming techniques, the sensor-level data are ltered spatially so that estimates of activity are obtained simultaneously at all locations in the brain. The lter coecients are chosen to minimize the lter output power subject to a linear constraint. The linear constraint forces the lter to pass brain activity from a specied location, while the power minimization attenuates activity originating at other locations. By solving the constrained minimization problem for each location in the source space, the power as a function of location is obtained.

The main assumption behind beamforming are that MEG data are generated by a set of discrete dipolar sources and that the time series of these sources are not linearly correlated. This latter assumption is the main limitation of beamformer approaches: two, perfectly correlated cortical sources will be self-canceling and hence will not be represented in the nal beamformer reconstruction. However, simulations and empir-ical results have shown that these algorithms are robust to moderate levels of source correlations.

The advantages of beamforming algorithms are twofold [9]. First, there is no need to specify the number, initial position, or orientation of dipoles a priori. If the assumptions are valid, the algorithm will correctly reconstruct the number and position of the sources. Second, because the weights are determined from the data covariance matrix, there is no need to average the MEG time series in the time domain. This makes beamforming ideal for studying time-, but not phase-, locked activity, such as the ERS and ERD oscillatory activity described above.

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Chapter 2 Data acquisition and preprocessing

The aim of this study was to investigate the brain's behavior during a typical cocktail party situation. To this purpose, complex auditory scenes composed of a mixture of human voice and natural environmental sounds were used for the stimulation and, as experimental task, subjects were asked to attend the voice or the background noise. In this chapter it is described the experimental environment used for the acquisition of MEG signals, the signal processing applied in order to obtain artifact-free data to be used in further analysis, and the realignment of the artifact-free signals to a target sensor array.

2.1 Experimental procedures

2.1.1 Subjects

Ten healthy subjects participated in the study (4 females and 6 males, all right-handed). They were graduate university students and were paid for their participation. All subjects were in good health with no history of hearing or neurological impairments and gave informed consent before the experiment. The study had been previously approved by the Ethical Committee of the Faculty of Psychology and Neuroscience, University of Maastricht, The Netherlands.

2.1.2 Stimuli

The stimuli were binaural and monaural complex auditory scenes, consisting of a combi-nation of environmental (E) and vocal (V) natural sounds. High quality binaural sound stimuli were recorded using two FG-23652-P16 (Knowles Electronics, Itasca, Illinois, USA) microphones. 96 KHz 24 bit binaural high quality recordings were made using an M-Audio MicroTrack 24/96 Pocket Digital Recorder. The sounds were recorded by placing the microphones in the ears of two human heads. Both environmental and vocal natural sounds were collected and a total of 96 of such binaural recordings were

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Chapter 2. Data acquisition and preprocessing selected. Compatible combinations were then created by mixing the vocal and environ-mental sounds to 58 dierent combined sounds and 29 combined target sounds. The length of combined stimuli was between 1000 and 2000 ms (mean = 1200 ms, std = 500 ms). The stimuli were downsampled and processed to 44100/16 bit using Adobe Audition (Adobe Systems Inc., San Josè, California, USA). The combined sounds were created in Adobe Audition using the mixed paste function. Monaural sounds were obtained by merging the 2 channels of the binaural stimuli. The sound amplitude en-velopes and average root-mean-square levels were matched using MATLAB 7.0.1 (The MathWorks, Natick, MA, USA).

2.1.3 Experimental design

A 2 x 2 event-related design was used, the two factors being the attention of the subject (to vocal or environmental sounds) and the nature of the stimulus (binaural or monaural). Therefore, the four conditions were:

1. combined monaural - attention to vocal sounds 2. combined binaural - attention to vocal sounds

3. combined monaural - attention to environmental sounds 4. combined binaural - attention to environmental sounds

The experiment consisted of 4 runs, each representing one condition, randomized. Be-fore each run the subject was instructed to attend to voices or environmental sounds. In each run 118 trials were recorded, with a silent interval of 1500 ms between each trial and the next one. Trial sequences were created by inserting target sounds in 10% of trials. These target sounds were discarded in the analysis.

In the following, binaural and monaural will be referred to as stereo and mono and the four conditions as mav, sav, mae, sae.

2.1.4 Data acquisition

MEG signals were recorded using a 275-channel whole head MEG system (CTF Systems Inc., Port Coquitlam, Canada) at the F.C. Donders Centre for Cognitive Neuroimaging. In addition, vertical and horizontal electrooculograms (EOGs) were recorded through appropriate bipolar montages, in order to be used for oine artifact rejection. Prior to and after MEG recording, positions and orientations of MEG sensors with respect to the subject's head were determined using coils positioned at the subject's nasion, and at the left and right ear canals. The information about sensors position was obtained by passing currents through the three coils and measuring the magnetic eld produced with the multichannel system. MEG data were low-pass ltered at 300 Hz and digitized continuously at 1200 Hz. For each subject, a full-brain anatomical magnetic resonance imaging (MRI) scan was acquired using a 1x1x1 mm3 _resolution

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Chapter 2. Data acquisition and preprocessing T1-weighted sequence. Brain imaging was performed with a 3 Tesla Siemens Allegra (Siemens, Erlangen, Germany) scanner (head setup) at the Maastricht Brain Imaging Center.

2.2 Data preprocessing

The Fieldtrip software package (http://www.ru.nl/fcdonders/eldtrip/) was used for all data analysis described in this chapter. It is an open source Matlab-based toolbox for EEG and MEG data analysis that has been developed at the F. C. Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands.

One subject was discarded from the analysis because of problems during the acquisition of MEG signals.

Three main analysis were done: an analysis of the time course and topographies of the evoked elds at the sensor level, an analysis of the sources underlying the observed evoked elds, and a time-frequency analysis of the MEG signal at the sensor level. All three analysis started with the same preprocessing steps: data segments of interest (or trials) were dened as such from the continuously recorded MEG signals; they were downsampled from 1200 to 300 Hz and band-pass ltered between 0.5 and 100 Hz. The power-line artifact was removed using the following procedure. All signals had been recorded continuously for the entire duration of the session. For each time segment of interest and each recording channel, rst a 10 s epoch was taken out of the continuous signal with the segment of interest in the middle. Then the discrete Fourier transform (DFT) of the 10 s epoch was calculated at 50 and 100 Hz without any tapering. 50 and 100 Hz sine waves with the amplitudes and phases as estimated by the respective DFTs were constructed and subtracted from the 10 s epoch. Finally, the segment of interest was cut out of the cleaned 10 s epoch. This method exploits the fact that the power-line artifact is of an almost perfectly constant frequency, therefore the 10 s epoch contains an integer number of cycles of the artifact frequencies, and all of the artifact energy is contained in the 50 Hz component and its harmonics. Moreover, subtracting the Fourier components on the epoch of 10 s length results in a spectral notch of only 0.1 Hz width.

2.3 Artifacts identication and removal

After preprocessing, data were checked for artifacts. Identication and removal of arti-facts are the most critical steps when preprocessing MEG signals, since the amplitude of an artifact may well exceed that of brain signals, thus obscuring the signals generated by task-relevant activities. Artifacts can be physiological or can result from the acqui-sition electronics. Physiological artifacts originate from eyes blinks, eyes movements, head movements and heart currents. Muscle artifacts from swallowing or contractions

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Chapter 2. Data acquisition and preprocessing

Figure 2.1: Examples of biological artifacts that can contaminate MEG recordings. Respira-tion artifact was elicited on purpose by putting a metallic piece over the chest of the subject [19].

in the neck and face areas can produce artifacts as well. Examples of physiological ar-tifacts are shown in Figure 2.1. Arar-tifacts related to the electronics are called SQUID jumps and they appear as spikes in the signal. Despite all measures that are always taken during the recording for preventing artifacts, such as instructing the subject not to blink during the trial, there will always be some artifacts in the raw data.

When dealing with artifacts, two strategies can be applied: artifacts rejection or ar-tifacts correction. Arar-tifacts rejection consists in setting a threshold depending on the kind of artifact that need to be detected and discarding all trials that exceed it. This procedure can be applied to raw data or after a transformation (e.g. ltering) which helps detect the artifact. This methods has two limitations [22]. First, it involves an arbitrary choice of magnitude threshold; second, rejection of trials with artifacts causes data loss and this can bias the results in some experiments because certain states of the subject are not included in the analysis.

Artifacts correction consists in compensating for artifacts by removing the artifact eld pattern from the data. The eectiveness of this method depends on the accuracy of

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Chapter 2. Data acquisition and preprocessing the artifact eld pattern used for the compensation. Independent component analysis (ICA, [21]) has been shown ([22, 23, 24]) to be an ecient artifacts correction tool for MEG recordings. Moreover, various methods ([22, 25]) have been proposed for artifact identication and automatic removal based on ICA and classication approaches. For the application of ICA, it is assumed that the MEG signal x(t) is generated from sources s(t) with a linear mixing procedure:

x(t) = As(t) (2.1)

where t is a vector of the sampling time with length T, x(t) = [x1(t), ...., xn(t)]T is

a n x T matrix of MEG signals recorded from n sensors; s(t) = [s1(t), ...., sm(t)]T

is a m x T matrix of m sources; A = [a1, ..., am] is an unknown n x m full-rank

mixing matrix. The application of ICA consists in performing the following demixing procedure on the MEG signals:

ˆs(t) = Wx(t) (2.2)

where ˆs(t) = [ˆs1(t), ..., ˆsm(t)] is a m-dimensional matrix of the independent

compo-nents, which represent the estimation of the sources s(t); W is a demixing matrix

W = ˆA+ (2.3)

where ˆA+denotes an estimate of the pseudo-inverse of the mixing matrix A. Several al-gorithms for performing ICA have been developed; these include JADE ([26]), infomax ICA ([21]) and FastICA ([27]).

It is important to note that the application of ICA on MEG signals makes at least the following four assumptions ([28]):

1. the underlying source signals are statistically independent; 2. the sources do not have a Gaussian distribution;

3. the mixing process at the sensors is instantaneous, linear and stationary; 4. the source signals are stationary.

The dierent nature of the sources of the artifacts from those of the actual brain signals allows the independence hypothesis to be fullled, as demonstrated by the analysis of the distribution of artifacts such as the cardiac cycle or the ocular activity, which has shown the statistical independence approximation to be accurate.

Typically, the artifacts encountered, as well as the dierent components in evoked elds studies, present clear Gaussian distributions. By contrast, problems with the non-Gaussianity assumption may be encountered when dealing with rhythmic activity in the brain. In fact, pure oscillatory activity has negative kurtosis, but in reality neural oscillations often comes as bursts of limited time span, whose kurtotic behavior is a

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Chapter 2. Data acquisition and preprocessing function of the duration of the burst itself. In the worst case, the global kurtosis may even be zero and the desired component is then interpreted as Gaussian. Methods based on the time correlation in the data ([29, 30]) may be a successful strategy to cope with this problem.

The validity of the instantaneous mixing model relies on the fact that most of the energy in MEG signals lies below 1 KHz, thus the quasi-static approximation of Maxwell equations holds ([4]) and there is no need for introducing any time delays. The linearity of the mixing follows from the Maxwell's equations as well. As regards the stationarity of the mixing process, it corresponds to the existence of a constant mixing matrix A and leads to sources with xed locations and orientations and amplitude varying with time, which is a source model widely used in the analysis of MEG data. Therefore, the use of constant mixing vectors ai is justied.

Although the nonstationarity of MEG signals is well documented ([31]), the assumption of stationarity of the source signals is not an issue for the application of ICA to the study of MEG signals. Indeed, the requirement of stationarity is necessary when considering the underlying source signals as stochastic processes; yet, in the implementation of batch ICA algorithms, the data are considered as random variables, whose distributions are estimated from the whole dataset. Thus, the non-stationarity of the signals is not really a violation of the assumptions of the model.

In the present study an hybrid method has been used for artifact identication and extraction: data were rst cleaned from artifacts deriving from eyes blinks, eyes move-ments and heart activity by using ICA; then, a visual inspection of the data trial by trial was performed in order to remove trials contaminated with artifacts caused by electronics or head movements. The use of ICA for eye and heart artifacts allowed to discard a low number of trials from each recording and hence contain loss of informa-tion.

2.3.1 Eye and heart artifacts correction with ICA

Infomax ICA was applied to the preprocessed MEG signals and the correlation coe-cient between each independent component and vertical and horizontal EOGs (vEOG and hEOG) was computed in order to detect the eye artifacts components. This pro-cedure was performed for each condition of each subject separately. For each data set, the number of components to be labeled as artifactual was set by looking at the eld patterns (given by the corresponding mixing vector ai) of the rst ve mostly

corre-lated ICs. In Figure 2.2 and 2.3 typical eld patterns and time course of eye artifacts components are shown.

Because the electrocardiogram (ECG) was not recorded during the acquisition of the MEG signal, it was not possible to identify the heart artifacts components by using the correlation approach. As a consequence, they were isolated by visually inspectioning the eld patterns of all the ICs, rst, and then the time course of the ones chosen as possible heart artifacts components. In Figure 2.4 and 2.5 typical eld patterns and time course of heart artifacts components are shown.

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Figure 2.2: Typical eld patterns of eye artifacts components.

Once the eye and heart artifacts ICs had been identied, MEG signals were recon-structed with the components which had not been labeled as artifacts. Thus, in the remixing matrix, the weight vectors of the identied artifactual ICs were set to zeros, such that

x0(t) = W+_cleanˆs(t) (2.4)

where x0

(t) is the reconstructed artifacts-free MEG signal for n sensors, W+ is the

pseudo-inverse of the demixing matrix W, and W+

clean is the n x m remixing matrix

with its columns corresponding to the artifactual ICs set to zeros; ˆs(t) is the activation matrix of ICs as mentioned above.

In Figure 2.6 an example of MEG signals before (red line) and after (blue line) the eye and heart artifacts correction is reported. The gure shows a single trial for some of the frontal and temporal sensors, together with the vertical and horizontal EOGs. Note as in the raw signal (red line) of some of the channels (e.g. MLF31, MLF41, MRT51) is clearly visible the eect of the heart beat in the form of spikes with the typical heart rate.

2.3.2 SQUID jumps and head movements artifacts rejection

After being corrected for eye and heart artifacts with ICA, MEG signals were visually inspected trial by trial in order to discard trials contaminated with SQUID jumps or artifacts caused by head movements. SQUID jumps were visible in MEG signals in the form of high amplitude spikes, while head movements caused slow drifts in the signals.

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Figure 2.3: a) Example of time course of the rst two eye independent components, compared to the relative EOGs. The time axis corresponds to the time required for recording the whole run. b) Enlarged details of the time courses, as highlighted in the grey boxes.

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Figure 2.4: Typical eld patterns of heart artifacts components.

Figure 2.5: a) Example of time course of the rst heart independent components. b) Enlarged detail of the time courses, as highlighted in the grey boxes.

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Figure 2.6: Example of MEG signals before (red line) and after (blue line) the eye and heart artifacts correction. The x axis is in seconds and the y axis is Tesla. In the last two panels, vEOG and hEOG are shown (x axis: seconds, y axis: Volts).

2.4 Realignment to a target sensor array

Contrary to EEG recordings, there is no standard location of the MEG sensors relative to the head. In fact, the positions of the MEG sensors are always constant with respect to each other, but not with respect to the head. As a consequence, realigning all recordings to one target sensor array is a prerequisite for averaging or comparing recordings of dierent subjects or dierent sessions on the same subject.

The realignment algorithm ([32]) used in the present work consists of the inverse com-putation of a hypothetical current distribution from the original data set using linear estimation theory and the subsequent forward computation of the outputs of the target sensors.

The basis of the algorithm is a set of current dipoles in a volume conductor model. The relationship between current dipoles and the surface recordings at a given instant in time is expressed by

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Figure 2.7: Realistically-shaped volume conductor model of one subject, original sensor array (red dots) and target sensor array (green dots). The black dots in the volume conductor model represent the current dipoles.

where y is a Mx1 vector of the magnetic elds measured at M sensors, m is a 3Nx1 vector of the x, y, and z components of the N dipolar sources placed in the volume conductor model, and L is a Mx3N transfer matrix, so-called lead eld matrix ([4]), whose columns represent solution to the forward problem for a set of N current dipoles of unite amplitude. For a given source conguration and volume conductor model, the lead eld matrix is determined by the conguration of MEG sensors. Thus, considering the same source conguration and volume conductor model for the original and the target sensor array, we have

yold= Loldm (2.6)

ynew= Lnewm (2.7)

where yold is the vector of MEG signals measured with the original sensor array, ynew

is the vector of MEG signals that would have been measured with the target sensor array, Lold and Lnew are the lead eld matrices for the original and the target sensor

array, respectively.

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ynew(t) = yold(t)LnewL−1old (2.8)

In the present study the average sensor array for all subjects was used as target sensor array for realigning the artifact-free data, and an individual realistically-shaped volume conductor model generated from the MRI images of the brain of each subject was used as basis for the lead eld computations. The lead elds were computed by extending the lead eld calculation for a spherical volume conductor by a superposition of basis functions and gradients of harmonic functions constructed from spherical harmonics ([33]). The brain of each subject was divided into a grid of 5 mm resolution and for each grid location the lead-eld was calculated. The realignment procedure was repeated for each condition and each subject in the study. In the next chapters the MEG signals realigned to the target sensor array will be referred to as the realigned signals.

In Figure 2.7 the volume conductor model, an example of original sensor array (red dots) and the target sensor array (green dots) are shown. Correspondent sensors in the two arrays are linked by a black line. The black dots in the volume conductor model represent the current dipoles used for the computation of the lead elds.

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Chapter 3 Evoked elds analysis

Several kinds of events, the most notably being sensory stimuli, can induce changes in the activity of neuronal populations that are both time- and phase- locked to the stimulus. These changes are generally called evoked-activity, or evoked-elds (EFs) in the case of MEG. Because of intrinsic and extrinsic noise in the MEG signal, averaging techniques are commonly used in order to detect such EFs. The basic assumption is that the evoked activity, or signal of interest, has a more or less xed time-delay to the stimulus, while the ongoing MEG activity behaves as additive noise. The averaging procedure will then enhance the signal-to-noise ratio. However, this simple and widely used model is just an approximation of the real situation.

In this chapter the sensor-level analysis of the evoked elds and the estimate of neural sources underlying the measured MEG signals will be presented.

3.1 Sensor-level analysis

A sensor-level analysis of time courses and topographies of grand-averaged evoked elds was performed as a guide for further analysis. For the analysis at the sensors level, the realigned signals were used in order to make possible comparisons between subjects and/or experimental conditions. For each condition of each subject in the study, the evoked elds were obtained by averaging all trials left after the artifact rejection procedure. In order to further increase the SNR, the signals were low-pass ltered with a cuto frequency of 40 Hz. The obtained evoked elds were then baseline corrected by subtracting from each sample measured after stimulus onset the mean amplitude during the baseline period. The pre-stimulus interval from -600 ms to -400 ms was chosen as baseline after an exploratory analysis of channel time-courses, that showed a pre-stimulus response in the latency from -400 ms to stimulus onset. This response was thought to reect a preparation stage of the subject, since he had been told to be in a specic state of attention during the stimulation.

MEG signals had been recorded with a 275 rst-order axial gradiometers system that measures the gradient of the magnetic eld in the radial direction, i.e. orthogonal to the scalp. The baseline-corrected signals were then transformed to a planar gradient

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Chapter 3. Evoked elds analysis

Figure 3.1: A comparison of topographic maps from axial (left) and planar (right) gradients.

conguration, by computing the eld gradient tangential to the scalp. The advantages of transforming the data to a planar gradient conguration are twofold [34]. First, the magnitude of the planar gradients can only take positive values and therefore relates to the absolute dipole moment independent of the dipole orientation. This is advantageous when averaging across subjects because the positive and negative elds in the axial gradiometers could partially cancel each other out because of interindividual dierences in dipole orientation. Second, the planar gradient conguration simplies the interpretation of topographic maps of MEG signals. In fact, with the axial gradient, dipolar elds produce two regions of maximum signal at either side of the source, while with the synthetic planar gradient the signal amplitude is largest directly above the source (see Figure 3.1). As a result, the planar gradient provides a more spatially focal power estimate compared to axial gradiometers.

The horizontal and the vertical components of the planar eld gradient were estimated for each sensor using the signals from the neighboring sensors (closer than 4 cm; typ-ically six sensors), and the two components were then combined using the root mean square (RMS), resulting in positive values. The planar gradient computed in this way approximates the signals measured by physical planar gradiometers systems.

For each experimental condition, the estimated planar gradients were pooled across subjects to obtain grand-averaged planar evoked elds. For a rst raw assessment of auditory activity, time courses of sensors located over the auditory areas were averaged separately for each brain hemisphere.

3.1.1 Results and discussion

The results of the source level analysis are summarized with the support of gures 3.2, 3.3, 3.4, and 3.5.

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Chapter 3. Evoked elds analysis Figure 3.2 shows an example time course of the grand-averaged planar evoked eld mea-sured by a sensor above the right auditory cortex. Consistent with the typical magnetic auditory evoked response ([35, 36]), the signal presents two transient responses, peaked respectively around 50 ms (M50) and 100 ms (M100) after stimulus onset, followed by a response sustained over several hundreds of milliseconds (SF, sustained eld).

Figure 3.2: Time course of the grand-averaged planar eld measured by a sensor over the right auditory cortex. The signal consists of two transient responses at 50 ms (M50) and 100 ms (M100), followed by a sustained response (SF). Shown is the absolute change with respect to baseline.

In Figure 3.3 is reported the time course of the grand-averaged planar gradients av-eraged over local sensor groups in the auditory areas. The right and left sides of the gure show the averages over the right and left hemisphere, respectively. Mono con-ditions (mav and mae), such as stereo concon-ditions (sav and sae), are plotted together for a visual comparison. In all conditions, the M100 response is stronger in the sensors of the right hemisphere, while it seems not to be a big dierence between the M100 responses of two conditions compared in the same hemisphere. Actually, the main dierences between mav and mae conditions seems to be in the sustained responses over the right hemisphere, where the mav response is stronger than the mae one. Sav and sae conditions seems to dier mainly in the sustained response as well, but in the left hemisphere and with sav response weaker than the sae one. However, this anal-ysis is only descriptive: statistical comparisons between experimental conditions were performed at the source level and will be described in the following.

Figure 3.4 shows the topographic distributions of the grand-averaged planar gradients for the experimental conditions voice (mav) and mono-attention-to-environment (mae). The topographies are obtained by averaging the planar data over the time window of interest. The rst visible eld pattern is the one of the M50 response, which appears bilaterally on the auditory areas. The M100 eld pattern (80

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-Chapter 3. Evoked elds analysis 120 ms) is still bilateral in both conditions. Around 180 ms after stimulus onset a new cluster of activity in sensors overlying the supplementary motor area (SMA) emerges and a new bilateral anterior source become visible in the auditory region. At about 240 ms after stimulus onset, the activity becomes more right-lateralized and starting from about 340 ms after stimulus onset only the activity in the posterior auditory re-gion of the right hemisphere is visible. In Figure 3.5 the topographic distributions of the conditions stereo-attention-to-voice (sav) and stereo-attention-to-environment (sae) are shown. Also in this case, the auditory eld pattern evolves from a bilateral poste-rior conguration to a bilateral anteposte-rior-posteposte-rior one and nally to a right-lateralized posterior source of activity.

Figure 3.3: Time course of the grand-averaged planar gradients averaged over local sensor groups overlying the auditory cortices. a) Selected sensors over the right and left hemispheres. b) Comparison between the averaged time courses of mav and mae evoked responses over the right and left hemispheres. c) Comparison between the averaged time courses of sav and sae evoked responses over the right and left hemispheres.

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3.2 Source-level analysis

The basic problem in MEG is how to estimate and localize the cortical sources corre-sponding to a certain distribution of magnetic elds recorded outside the head. This is known as the MEG inverse problem and, unfortunately, it is an ill-posed problem that has no unique solution, i.e. there are innite current distributions that can explain the measurements. Because of the nonuniqueness of the inverse problem, it is neces-sary to seek an assumption set that is both realistic and renders the problem soluble. In view of this situation, several source reconstruction algorithms, each employing a dierent set of assumptions, have been proposed in order to solve the inverse problem (see Chapter 1 for an overview of the most widely used methods).

In the present study, attention was focused on beamforming and minimum-norm algo-rithms (MNE), which do not need to specify the number, initial position, or orientation of dipoles a priori. This was the main reason to prefer this localization techniques to dipole-tting methods such as ECD. Because of the nature of the MEG signal, the minimum-norm approach was preferred to beamforming. In fact, the performance of beamformers degrades in the presence of highly correlated sources, and bilateral audi-tory cortices are highly coherent in their activation ([37]). As a consequence, beam-former reconstructions of auditory evoked elds commonly fail, attenuating the two true sources from each primary auditory cortex and often erroneously placing a single low-amplitude source centered between them.

In the following a description of the MNE algorithm and the source reconstruction procedure will be reported.

3.2.1 Minimum-norm estimate

An individual active neuron is reasonably modeled as a current dipole and active areas in the cortex can be modeled by an equivalent current dipole. The relationship between dipole models and the surface recordings is obtained as follows. Let y be an Mx1 vector composed of the magnetic elds measured at the M sensor sites at a given instant in time associated with a single dipole source. If this source has location represented by 3x1 vector q, then

y = L(q)m(q) (3.1)

where m(q) is a 3x1 vector whose elements are the x,y, and z components of the dipole moment at the instant in time when y is measured, and L(q) is a Mx3 transfer matrix, so-called lead eld matrix ([4]), whose columns represent solution to the forward problem for a dipole of unit amplitude. That is, the rst column of L(q) is the eld at the sensors due to a dipole source at location q having unit moment in the x direction and zero moment in y and z directions. Similarly, the second and third columns represent the eld due to sources with unit moment in y and z directions, respectively. In a physical sense, L(q) contains information about the material and geometrical

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Chapter 3. Evoked elds analysis proprieties of the medium in which the sources are submerged, thus it is determined by the volume conduction model, besides the coil conguration of the sensors.

Because the medium is linear, the measured magnetic eld for the simultaneous ac-tivation of multiple dipoles located at qi is obtained by linear superposition of the

individual contributions. Suppose y is composed of magnetic elds due to N active dipole sources and noise. Then

y =

N

X

i=1

L(qi)m(qi) + n = Lm + n (3.2)

where L is the Mx3N lead eld matrix, m is the 3Nx1 vector of the x, y and z com-ponents of the N dipolar sources, and n is the measurement noise. The i-th row of the lead eld matrix describes the sensitivity distribution of the i-th sensor. This model can be readily extended to include a time component t.

The MEG inverse problem consists of solving Eq. 3.2 for the unknown source distri-bution m. If the source distridistri-bution m contains more independent parameters than there is independent information in the recording y (e.g., if N > M), then the source amplitudes represented by m cannot be estimated independently of each other ([38]). In other words, Eq. 3.2 is underdetermined and the corresponding inverse problem is ill-posed. A unique solution to the inverse problem stated in Eq. 3.2 can be found by constraining the solution to have minimal power, besides explaining the measured data. This is the minimun-norm source estimate. The constraints on the solution and the predicted data can be formulated as ([39])

ˆ

mTCmm = minˆ (3.3)

for the solution, and

(L ˆm − y)T(L ˆm − y) = min (3.4)

for the predicted data. ˆmis the estimated solution, C_mis the source covariance matrix, L ˆm are the predicted data, and y are the measured data.

If the matrix Cm is positive denite (and therefore invertible), the solution to this

problem is

ˆ

m = C−1_m LT(LC−1_m LT)−1y (3.5) The matrix Cm might be used to incorporate a priori information on the spatial

distri-bution of the source currents. For example, if appropriate fMRI results are available, spatial information about the expected active areas can be incorporated into the MNE by employing a diagonal Cm with larger elements at the locations of signicant fMRI

activity ([40]). However, if sources can be expected at any location in the source space, each location must be given equal weight. In this case, Cm is the identity matrix, and

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ˆ

m = LT(LLT)−1y (3.6)

Eq. 3.6 represents the MNE solution to the inverse problem in the case that the data shall be explained completely by the solution itself. However, in realistic situations, noise is present in the data. One approach to account for noise in the data is to substitute the constraint on the predicted data by

(L ˆm − y)TCn(L ˆm − y) = ε > 0 (3.7)

where Cn is the covariance matrix of the noise and e reects the part of the data

that shall remain unexplained, that is, which is due to noise. The MNE solution then changes to the noise-normalized MNE

ˆ

m = C−1_m LT(LC−1_m LT + λC−1_n )−1y = Wy (3.8) where l is the regularization parameter which needs to be determined such that e reaches an optimal value.

The MNE is known to have a bias towards supercial currents, caused by the attenua-tion of the MEG lead elds with increasing source depth. It is possible to compensate for this tendency by scaling the columns of L with a function increasing monotonically with the source depth ([40]). The solution obtained is called depth-weighted MNE.

3.2.2 Lead-eld computation and head models

As shown in the previous paragraph (see, e.g., Eq. 3.6), the MNE solution of the inverse problem requires the knowledge of the lead-eld matrix, i.e. the solution of the forward problem for the assumed source conguration. In general, computation of the lead-eld matrix is an important component of any source reconstruction method ([41]).

The forward problem involves computing the external magnetic eld at a nite set of sensor locations for a putative source conguration. The solution of the forward problem is inherently complicated by the fact that the magnetic eld induced by a sin-gle current element depends on the characteristics of the volume conductor, the head. Actually, since only a relatively small proportion of the currents ow in the poorly conducting skull, it is sucient for MEG to model only the intracranial space ([19]). The simplest volume conduction model is a single- or multi-shell sphere with homoge-neous and isotropic conductivity in the inside. In this case, the forward problem can be solved exactly in closed form ([42]). Although the spherical models work reasonably well, more accurate solutions to the forward problem are obtained with realistically-shaped volume conductor models, derived from high-resolution volumetric MR brain images. In this case numerical methods are required for solving the eld equations. One approach in this sense is assuming that the volume conductor consists of piecewise homogeneous and isotropic realistically-shaped compartments such as grey matter and

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Chapter 3. Evoked elds analysis white matter. Under this assumption, the eld equations, which are in general dif-ferential equations in the whole volume, can be reduced to integral equations dened solely on the surfaces of the compartments. These equations can be solved numerically by the boundary element method (BEM). Drawbacks of BEM are that it is computa-tionally quite costly and requires a large amount of memory. Furthermore, while it is an improvement on the spherical model, the BEM method still assume homogeneity and isotropy within each region of the head. The nite element methods (FEM) can in principle deal with these factors, allowing to solve the forward problem for arbitrary (and not piecewise homogeneous and isotropic) conductivity distributions. Actually, FEM methods are limited by in vivo conductivity and anisotropy parameters that are mostly unknown, besides the large computational eort required. Thus, the BEM is a compromise between over-simplied, spherically symmetric models, that reect only mean conductivities but not the shape of the compartments, and overly complex models for which detailed real tissue data are not available.

3.2.3 Source estimation

To localize the current sources underlying the evoked elds, a cortically constrained depth-weighted noise-normalized MNE was performed. For source reconstruction, data from the measured axial sensors (not the registered data or the planar gradient esti-mate) were used. Source reconstruction and statistical analysis were performed with BrainVoyager QX 2.7 (Brain Innovation, Maastricht, The Netherlands).

The basic approach of MNE consists of distributing dipoles over a predened volumetric grid; since primary sources are widely believed to be restricted to the cortex, the source distribution can be plausibly constrained to sources lying on the cortical surface that has been extracted from an anatomical MR image of the subject ([43]). Following the segmentation of the MR volume, dipolar sources are placed at each node of a triangular tessellation of the surface of the cortical mantle. Since the apical dentrites that produce the measured elds are oriented normal to the surface, it is possible to further constrain each of these elemental dipolar sources to be normal to the surface. In the present work, a common source space was prepared from the T1-weighted volumetric images of the standard brain from the Montreal Neurological Institute (MNI), the MNI-152. After importing these images to the Brain Voyager QX software, the surface of both hemispheres was reconstructed from the segmented images and the surface of the head from unsegmented images.

The mesh of the MNI-152 head was used to register the MEG 275-channel conguration to the source space. Positions and orientations of MEG sensors with respect to the subject's head were given in a head-coordinate system with the principal (X, Y, Z) axes going through external landmarks corresponding to the subject's nasion, left and right ear canals (Figure 3.6 a). These external landmarks had been determined by passing currents through three small coils placed on them and measuring the magnetic eld produced. The vertices of the source space were expressed in the coordinate system shown in Figure 3.6 b, with the X axis going from anterior to posterior, the Y axis from superior to inferior and the Z axis from right to left. The same anatomical landmarks

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Chapter 3. Evoked elds analysis identied on the subject's head were dened on the mesh of the MNI-152 head and the registration was performed using an ane transformation. See Figure 3.7 for the locations of the 275 sensors and ducial landmarks placed on the MNI template head surface.

Figure 3.6: a) Head-coordinate system used to express the positions and orientations of MEG sensors with respect to the subject's head. The midpoint between the left and right pre-auricular points is dened as the coordinate origin. The axis directed away from the origin toward the left pre-auricular point is dened as the y axis and that from the origin to the nasion is the x axis. The z axis is dened as the axis perpendicular to both these axes and is directed from the origin to the vertex. b) Coordinate system used to express the positions of the vertices of the source space.

The mesh of the cerebral cortex was used to prepare the source space. More in detail, after anatomical normalization to the Talairach space ([44]), the white matter volume was segmented from the T1-weighted MNI-152 scans and the white-gray matter bound-ary was identied for cortical surface reconstruction. From the inner cortical boundbound-ary, a dense cortex mesh was reconstructed with 80,000 vertices located along the modeled surface. Although a large number of points are required to represent the cortex ac-curately because of its heavily convoluted geometry, the use of a dense point set may be unnecessary because of the relatively poor spatial resolution of MEG. Therefore, the cortex mesh was simplied by means of a geometry-preserving mesh decimation algorithm resulting in a new mesh with only 2500 vertices per hemisphere (Figure 3.8). Starting from the common source space, depth-weighted noise-normalized MNE so-lutions (W in Eq. 3.8) were estimated separately for each subject and each exper-imental condition. A dipolar source was placed in each vertex of the cortex mesh representing the source space and the lead-eld for the MEG channel conguration was computed, assuming a spherical volume conduction model and free orientation for

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Figure 3.7: Registration of the MEG 275-channel conguration to the source space. a) MNI template head surface with white dots representing the ducial markers used for the registration. b) MEG channels (red dots) before (left) and after (right) registration to the head surface. The purple dot represent the nasion landmark in the subject's head-coordinate system.

the point sources. That means that for each vertex, all three orientations (X, Y, and Z) were considered for a dipolar sources placed in this vertex and three lead-elds were computed, one for each orientation. Figure 3.9 shows an example of the X component of the lead-eld, computed for the sensor represented in red.

Because no hypothesis about source location was made, the identity matrix was used as source covariance matrix (Cm in Eq. 3.8). The noise covariance matrix (Cn in Eq. 3.8)

was estimated from the prestimulus data in the interval from -800 ms to -400 ms before the stimulus onset. For each subject, all trials from all conditions were used. In fact, it is reasonable to assume that the noise properties do not change between experimental conditions, thus the statistical power of the estimate is increased by using the highest possible number of trials.

For the regularization parameter (l in Eq. 3.8) the following formula ([45]) was used: λ = trace(LCmL

T₎

trace(Cn)SN R2

(3.9) This formula allows adjusting the regularization parameter in terms of a realistic guess

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Figure 3.8: 5000 vertices cortex mesh representing the source space.

of the SNR of the current sources. A xed value of 5 was used for the SNR, because this reects the value in many evoked response experiments ([45]).

For the depth-regularization of the MNE solution, the following function ([45]) was used for scaling the columns of the lead-eld matrix

fi = 1/(aT3i−2a3i−2+ aT3i−1a3i−1+ aT3ia3i)γ (3.10)

where apis the p-th column of L, the subscript i denotes the i-th dipole, and g is a

tunable parameter called depth-weighting factor. A xed value of 0.5 was used for g, as suggested in [47].

The linear inverse operator W was used for projecting the recorded MEG data from the channel to the cortex source space by

m(t) = Wy(t) (3.11)

For each subject and each experimental condition, all the trials from the artifact-free data set were rst averaged for obtaining the evoked response and, then, projected to the MNI cortex source space. At each vertex of the source space, a root-mean-square (RMS) power time-series was generated by taking the RMS of the three dipole orien-tation components (X,Y, and Z). For each latency within the interval from 0 ms to 800 ms after the stimulus onset, a surface map of the evoked activity was obtained by subtracting the mean power in the baseline period (from -600 ms to -400 ms ) from the projected RMS power at that latency. However, it is important to note that these maps were only descriptive and had no statistical signicance, seeing that they were derived from the projection of pre-averaged channel data. In order to obtain statistical maps of the main and dierential eects, single trial data from each subject and each

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exper-Chapter 3. Evoked elds analysis

Figure 3.9: Example of lead-eld, computed for the sensor represented in red and with dipolar sources oriented in the X direction (see Figure 3.6 b).

imental condition were projected to the cortex source space. The projected data were used for producing xed-eects statistic of the main and dierential eects of interest in single- and multi-subjects analysis; main and dierential eects in a given time la-tency were evaluated via t-tests at each vertex of the source space. Cortical statistical maps were generated for three latencies, corresponding to the main components of the evoked response, i.e. 30 ms - 70 ms for the M50 component, 70 ms - 150 ms for the M100 component and 150 ms - 360 ms for the sustained response. For each latency, a t-map was generated for the main eects of all conditions and for the dierential eects between mav and mae, and sav and sae conditions. For the statistical comparison of mono conditions three subjects were excluded from the original sample of 9 subjects, because of corrupted MEG acquisition during the mae condition.

3.2.4 Results and discussion

MNE localized sources of activity in left and right auditory cortex, though for all conditions the activation was stronger in the right hemisphere. Figure 3.10 shows the right hemisphere of the grand-averaged cortical surface map of evoked activity in the latency 80 ms -120 ms, for the mav condition.

The multi-subject statistical xed-eects analysis revealed no signicant dierential eects between sav and sae conditions within all of the three investigated latencies. By contrast, statistical comparison of mav and mae conditions in the interval 150 ms - 360 ms was able to identify one signicant region (p< 0.009, uncorrected) for the distribution of the dierential eects, providing the MEG source that exhibited the