Electroencephalographic Brain Dynamics Following Manually Responded Visual Targets

Scalp-recorded electroencephalographic (EEG) signals produced by partial synchronization of cortical field activity mix locally synchronous electrical activities of many cortical areas. Analysis of event-related EEG signals typically assumes that poststimulus potentials emerge out of a flat baseline. Signals associated with a particular type of cognitive event are then assessed by averaging data from each scalp channel across trials, producing averaged event-related potentials (ERPs). ERP averaging, however, filters out much of the information about cortical dynamics available in the unaveraged data trials. Here, we studied the dynamics of cortical electrical activity while subjects detected and manually responded to visual targets, viewing signals retained in ERP averages not as responses of an otherwise silent system but as resulting from event-related alterations in ongoing EEG processes. We applied infomax independent component analysis to parse the dynamics of the unaveraged 31-channel EEG signals into maximally independent processes, then clustered the resulting processes across subjects by similarities in their scalp maps and activity power spectra, identifying nine classes of EEG processes with distinct spatial distributions and event-related dynamics. Coupled two-cycle postmotor theta bursts followed button presses in frontal midline and somatomotor clusters, while the broad postmotor “P300” positivity summed distinct contributions from several classes of frontal, parietal, and occipital processes. The observed event-related changes in local field activities, within and between cortical areas, may serve to modulate the strength of spike-based communication between cortical areas to update attention, expectancy, memory, and motor preparation during and after target recognition and speeded responding

Single Epoch Analysis and Bi-hemisphere Study of Magnetoencephalographic (MEG) Signals using Vector Signal Transformation V3 and Magnetic Field Tomography (MFT)

The biomagnetic inverse problem has no unique solution, nevertheless even a cursory look at the features shown in raw signal can often suffice to highlight strong superficial activity. To do a proper single epoch analysis is normally prohibitively expensive in terms of computing demands. Hence the original aim of this thesis was to use simple efficient signal transformations to characterize superficial generators and contrast the single epoch signature with that extracted from the average signal. The results have intrigued us sufficiently to go beyond the original goal and extract very preliminary estimates of activity across the cerebral hemisphere in single trials. The original tool, and one that we have used for much of the work, is a simple vector signal transformation called V3. This signal transformation highlights nearby sources; it is a crude but quick estimator of generators directly from the raw MEG signals. Together with Magnetic Field Tomography (MFT), which relies on distributed source analysis of the MEG signals, we have tackled the following specific problems relating to aspects of normal brain function: efficient estimation of generators of magnetic fields; relationship between the average signal and single trials; and interhemispheric differences and relationship between the activity in the left and right hemispheres of the brain. During the project, we have used as examples auditory evoked MEG measurements obtained from two multichannel systems and applied the V3 and MFT analysis to both the average and single trial signals. In particular, we chose the 40-Hz (or gamma band) auditory response as the study subject. We found that in single epochs similar patterns of high frequency activity are observed in the area around the auditory cortex well before, close to and well after stimulus onset; the sequence of events observed in the average can only represent the evolution of events in single trials in a statistical way; and deep and central areas of the brain may be the seeds for the main deflections observed in the auditory responses

Fast Robust Subject-Independent Magnetoencephalographic Source Localization Using an Artificial Neural Network

We describe a system that localizes a single dipole to reasonable accuracy from noisy magnetoencephalographic (MEG) measurements in real time. At its core is a multilayer perceptron (MLP) trained to map sensor signals and head position to dipole location. Including head position overcomes the previous need to retrain the MLP for each subject and session. The training dataset was generated by mapping randomly chosen dipoles and head positions through an analytic model and adding noise from real MEG recordings. After training, a localization took 0.7 ms with an average error of 0.90 cm. A few iterations of a Levenberg-Marquardt routine using the MLP output as its initial guess took 15 ms and improved accuracy to 0.53 cm, which approaches the natural limit on accuracy imposed by noise. We applied these methods to localize single dipole sources from MEG components isolated by blind source separation and compared the estimated locations to those generated by standard manually assisted commercial software

MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG.

NUTMEG is a source analysis toolbox geared towards cognitive neuroscience researchers using MEG and EEG, including intracranial recordings. Evoked and unaveraged data can be imported to the toolbox for source analysis in either the time or time-frequency domains. NUTMEG offers several variants of adaptive beamformers, probabilistic reconstruction algorithms, as well as minimum-norm techniques to generate functional maps of spatiotemporal neural source activity. Lead fields can be calculated from single and overlapping sphere head models or imported from other software. Group averages and statistics can be calculated as well. In addition to data analysis tools, NUTMEG provides a unique and intuitive graphical interface for visualization of results. Source analyses can be superimposed onto a structural MRI or headshape to provide a convenient visual correspondence to anatomy. These results can also be navigated interactively, with the spatial maps and source time series or spectrogram linked accordingly. Animations can be generated to view the evolution of neural activity over time. NUTMEG can also display brain renderings and perform spatial normalization of functional maps using SPM's engine. As a MATLAB package, the end user may easily link with other toolboxes or add customized functions

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

Analysis of the structure of time-frequency information in electromagnetic brain signals

This thesis encompasses methodological developments and experimental work aimed at revealing information contained in time, frequency, and time–frequency representations of electromagnetic, specifically magnetoencephalographic, brain signals. The work can be divided into six endeavors. First, it was shown that sound slopes increasing in intensity from undetectable to audible elicit event-related responses (ERRs) that predict behavioral sound detection. This provides an opportunity to use non-invasive brain measures in hearing assessment. Second, the actively debated generation mechanism of ERRs was examined using novel analysis techniques, which showed that auditory stimulation did not result in phase reorganization of ongoing neural oscillations, and that processes additive to the oscillations accounted for the generation of ERRs. Third, the prerequisites for the use of continuous wavelet transform in the interrogation of event-related brain processes were established. Subsequently, it was found that auditory stimulation resulted in an intermittent dampening of ongoing oscillations. Fourth, information on the time–frequency structure of ERRs was used to reveal that, depending on measurement condition, amplitude differences in averaged ERRs were due to changes in temporal alignment or in amplitudes of the single-trial ERRs. Fifth, a method that exploits mutual information of spectral estimates obtained with several window lengths was introduced. It allows the removal of frequency-dependent noise slopes and the accentuation of spectral peaks. Finally, a two-dimensional statistical data representation was developed, wherein all frequency components of a signal are made directly comparable according to spectral distribution of their envelope modulations by using the fractal property of the wavelet transform. This representation reveals noise buried processes and describes their envelope behavior. These examinations provide for two general conjectures. The stability of structures, or the level of stationarity, in a signal determines the appropriate analysis method and can be used as a measure to reveal processes that may not be observable with other available analysis approaches. The results also indicate that transient neural activity, reflected in ERRs, is a viable means of representing information in the human brain.reviewe

Spatio-temporal evolution of interictal epileptic activity : a study with unaveraged multichannel MEG data in association with MRIs.

This thesis addresses issues relating to MEG modelling, analysis and interpretation of results. A source model employing current density distributions, namely Magnetic Field Tomography (MET), is used to obtain the MEG results. The first issue of concern refers to the registration of MEG data with structural MR images in an attempt to improve the localisation capability of MEG/MET. Simulations testing some spatial and tem poral aspects of the reconstruction capability of MET are also provided. A novel way of conducting MET studies in depth is suggested and implemented: the iterative use of a source space designed to cover deep situated structures on either side of the brain. The main bulk of this thesis is concerned with research into interictal epileptic activity as recorded by means of multichannel MEG system s and analysed using MET. The major aim is to investigate whether or not MET analysis of unaveraged MEG data (single epochs) is feasible in cases of pathophysiological signals and more specifically interictal signals from patients with epilepsy of a complex partial type. The investigation is undertaken against the "traditional" view of the impropriety and absurdity of using single epoch records in the MEG analysis due to noise dominance; we provide evidence that analysis of single, unaveraged epileptic spikes is actually feasible: we demonstrate spatio-temporal coherence in the MET results of the various single interictal events and show that activity extracted from the "averaged event" is made up of activity contributions which occur intermittently and at variable latencies. Our statements are drawn from the study of both superficial and deep activity

Scanning Reduction Strategy in MEG/EEG Beamformer Source Imaging

MEG/EEG beamformer source imaging is a promising approach which can easily address spatiotemporal multi-dipole problems without a priori information on the number of sources and is robust to noise. Despite such promise, beamformer generally has weakness which is degrading localization performance for correlated sources and is requiring of dense scanning for covering all possible interesting (entire) source areas. Wide source space scanning yields all interesting area images, and it results in lengthy computation time. Therefore, an efficient source space scanning strategy would be beneficial in achieving accelerated beamformer source imaging. We propose a new strategy in computing beamformer to reduce scanning points and still maintain effective accuracy (good spatial resolution). This new strategy uses the distribution of correlation values between measurements and lead-field vectors. Scanning source points are chosen yielding higher RMS correlations than the predetermined correlation thresholds. We discuss how correlation thresholds depend on SNR and verify the feasibility and efficacy of our proposed strategy to improve the beamformer through numerical and empirical experiments. Our proposed strategy could in time accelerate the conventional beamformer up to over 40% without sacrificing spatial accuracy

Attenuated mismatch negativity in patients with first-episode antipsychotic-naive schizophrenia using a source-resolved method

Background: Mismatch negativity (MMN) is a measure of pre-attentive auditory information processing related to change detection. Traditional scalp-level EEG methods consistently find attenuated MMN in patients with chronic but not first-episode schizophrenia. In the current paper, we use a source-resolved method to assess MMN and hypothesize that more subtle changes can be identified with this analysis method. Method: Fifty-six first-episode antipsychotic-naïve schizophrenia (FEANS) patients (31 males, 25 females, mean age 24.6) and 64 matched controls (37 males, 27 females, mean age 24.8) were assessed for duration-, frequency- and combined-type MMN and P3a as well as 4 clinical, 3 cognitive and 3 psychopathological measures. To evaluate and correlate MMN at source-level, independent component analysis (ICA) was applied to the continuous EEG data to derive equivalent current dipoles which were clustered into 19 clusters based on cortical location. Results: No scalp channel group MMN or P3a amplitude differences were found. Of the localized clusters, several were in or near brain areas previously suggested to be involved in the MMN response, including frontal and anterior cingulate cortices and superior temporal and inferior frontal gyri. For duration deviants, MMN was attenuated at the right superior temporal gyrus in patients compared to healthy controls (p = 0.01), as was P3a at the superior frontal cortex (p = 0.01). No individual patient correlations with clinical, cognitive, or psychopathological measures survived correction for multiple comparisons. Conclusion: Attenuated source-localized MMN and P3a peak contributions can be identified in FEANS patients using a method based on independent component analysis (ICA). This indicates that deficits in pre-attentive auditory information processing are present at this early stage of schizophrenia and are not the result of disease chronicity or medication. This is to our knowledge the first study on FEANS patients using this more detailed method. Keywords: Mismatch negativity, Schizophrenia, First episode, EEG, IC