Simultaneous EEG-fMRI Analysis with Application to Detection of Seizure Signal Sources

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Characterization of Neuroimage Coupling Between EEG and FMRI Using Within-Subject Joint Independent Component Analysis

The purpose of this dissertation was to apply joint independent component analysis (jICA) to electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to characterize the neuroimage coupling between the two modalities. EEG and fMRI are complimentary imaging techniques which have been used in conjunction to investigate neural activity. Understanding how these two imaging modalities relate to each other not only enables better multimodal analysis, but also has clinical implications as well. In particular, Alzheimer’s, Parkinson’s, hypertension, and ischemic stroke are all known to impact the cerebral blood flow, and by extension alter the relationship between EEG and fMRI. By characterizing the relationship between EEG and fMRI within healthy subjects, it allows for comparison with a diseased population, and may offer ways to detect some of these conditions earlier. The correspondence between fMRI and EEG was first examined, and a methodological approach which was capable of informing to what degree the fMRI and EEG sources corresponded to each other was developed. Once it was certain that the EEG activity observed corresponded to the fMRI activity collected a methodological approach was developed to characterize the coupling between fMRI and EEG. Finally, this dissertation addresses the question of whether the use of jICA to perform this analysis increases the sensitivity to subcortical sources to determine to what degree subcortical sources should be taken into consideration for future studies. This dissertation was the first to propose a way to characterize the relationship between fMRI and EEG signals using blind source separation. Additionally, it was the first to show that jICA significantly improves the detection of subcortical activity, particularly in the case when both physiological noise and a cortical source are present. This new knowledge can be used to design studies to investigate subcortical signals, as well as to begin characterizing the relationship between fMRI and EEG across various task conditions

Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill posed problem that commonly arises when modelling real world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of (ie, hypotheses about) network architectures and implicit coupling functions in terms of their Bayesian model evidence. These methods are collectively referred to as dynamical casual modelling (DCM). We focus on a relatively new approach that is proving remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems

The gamma model analysis: Introducing a novel scoring method of event-related potentials

Research using the event-related potential (ERP) method to investigate cognitive processes has usually focused on the analysis of either individual peaks or the area under the curve as components of interest. These approaches, however, cannot analyse the substantial variation in size and shape across individual waveforms. The aim of my thesis is thus to introduce the gamma model analysis (GMA). The GMA addresses these specific restrictions of the usually applied methods and enables the analysis of additional time-dependent and shape-related information on ERP components by fitting mathematically computed gamma probability density function (PDF) waveforms to an ERP. The advantage of the GMA is demonstrated in a simulation study and a digit flanker task, as well as a force production task. The data of the digit flanker task is also used to examine a potential limitation of the GMA, namely the inability of the gamma PDF to execute a sign change. Finally, the gamma PDF was compared with three other PDFs concerning their goodness of fit. The different gamma model parameters were sensitive to various experimental manipulations across the empirical studies. Moreover, the GMA revealed several additional interrelated but non-redundant parameters compared to the classical methods, which were predictive of different aspects of behaviour, allowing for a more nuanced analysis of the cognitive processes. The GMA provides an elegant method for extracting easily interpretable indices for the rise and decline of the components that complement the classical parameters. This approach, therefore, provides a novel toolset to better understand the exact relationship between ERP components, behaviour, and cognition

Identification of audio evoked response potentials in ambulatory EEG data

Electroencephalography (EEG) is commonly used for observing brain function over a period of time. It employs a set of invasive electrodes on the scalp to measure the electrical activity of the brain. EEG is mainly used by researchers and clinicians to study the brain’s responses to a specific stimulus - the event-related potentials (ERPs). Different types of undesirable signals, which are known as artefacts, contaminate the EEG signal. EEG and ERP signals are very small (in the order of microvolts); they are often obscured by artefacts with much larger amplitudes in the order of millivolts. This greatly increases the difficulty of interpreting EEG and ERP signals.Typically, ERPs are observed by averaging EEG measurements made with many repetitions of the stimulus. The average may require many tens of repetitions before the ERP signal can be observed with any confidence. This greatly limits the study and useof ERPs. This project explores more sophisticated methods of ERP estimation from measured EEGs. An Optimal Weighted Mean (OWM) method is developed that forms a weighted average to maximise the signal to noise ratio in the mean. This is developedfurther into a Bayesian Optimal Combining (BOC) method where the information in repetitions of ERP measures is combined to provide a sequence of ERP estimations with monotonically decreasing uncertainty. A Principal Component Analysis (PCA) isperformed to identify the basis of signals that explains the greatest amount of ERP variation. Projecting measured EEG signals onto this basis greatly reduces the noise in measured ERPs. The PCA filtering can be followed by OWM or BOC. Finally, crosschannel information can be used. The ERP signal is measured on many electrodes simultaneously and an improved estimate can be formed by combining electrode measurements. A MAP estimate, phrased in terms of Kalman Filtering, is developed using all electrode measurements.The methods developed in this project have been evaluated using both synthetic and measured EEG data. A synthetic, multi-channel ERP simulator has been developed specifically for this project.Numerical experiments on synthetic ERP data showed that Bayesian Optimal Combining of trial data filtered using a combination of PCA projection and Kalman Filtering, yielded the best estimates of the underlying ERP signal. This method has been applied to subsets of real Ambulatory Electroencephalography (AEEG) data, recorded while participants performed a range of activities in different environments. From this analysis, the number of trials that need to be collected to observe the P300 amplitude and delay has been calculated for a range of scenarios

Motor-evoked potentials reveal a motor-cortical readout of evidence accumulation for sensorimotor decisions

Many everyday activities require time-pressured sensorimotor decision making. Traditionally, perception, decision, and action processes were considered to occur in series, but this idea has been successfully challenged, particularly by neurophysiological work in animals. However, the generality of parallel processing requires further elucidation. Here, we investigate whether the accumulation of a decision can be observed intrahemispherically within human motor cortex. Participants categorized faces as male or female, with task difficulty manipulated using morphed stimuli. Transcranial magnetic stimulation, applied during the reaction-time interval, produced motor-evoked potentials (MEPs) in two hand muscles that were the major contributors when generating the required pinch/grip movements. Smoothing MEPs using a Gaussian kernel allowed us to recover a continuous time-varying MEP average, comparable to an EEG component, permitting precise localization of the time at which the motor plan for the responding muscle became dominant. We demonstrate decision-related activity in the motor cortex during this perceptual discrimination task, suggesting ongoing evidence accumulation within the motor system even for two independent actions represented within one hemisphere

Rapid neural processing of grammatical tone in second language learners

The present dissertation investigates how beginner learners process grammatical tone in a second language and whether their processing is influenced by phonological transfer. Paper I focuses on the acquisition of Swedish grammatical tone by beginner learners from a non-tonal language, German. Results show that non-tonal beginner learners do not process the grammatical regularities of the tones but rather treat them akin to piano tones. A rightwards-going spread of activity in response to pitch difference in Swedish tones possibly indicates a process of tone sensitisation. Papers II to IV investigate how artificial grammatical tone, taught in a word-picture association paradigm, is acquired by German and Swedish learners. The results of paper II show that interspersed mismatches between grammatical tone and picture referents evoke an N400 only for the Swedish learners. Both learner groups produce N400 responses to picture mismatches related to grammatically meaningful vowel changes. While mismatch detection quickly reaches high accuracy rates, tone mismatches are least accurately and most slowly detected in both learner groups. For processing of the grammatical L2 words outside of mismatch contexts, the results of paper III reveal early, preconscious and late, conscious processing in the Swedish learner group within 20 minutes of acquisition (word recognition component, ELAN, LAN, P600). German learners only produce late responses: a P600 within 20 minutes and a LAN after sleep consolidation. The surprisingly rapid emergence of early grammatical ERP components (ELAN, LAN) is attributed to less resource-heavy processing outside of violation contexts. Results of paper IV, finally, indicate that memory trace formation, as visible in the word recognition component at ~50 ms, is only possible at the highest level of formal and functional similarity, that is, for words with falling tone in Swedish participants. Together, the findings emphasise the importance of phonological transfer in the initial stages of second language acquisition and suggest that the earlier the processing, the more important the impact of phonological transfer