Integration of EEG-FMRI in an Auditory Oddball Paradigm Using Joint Independent Component Analysis

The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. The overall objective of this dissertation is to determine the sensitivity and limitations of joint independent component analysis (jICA) within-subject for integration of ERP and fMRI data collected simultaneously in a parametric auditory oddball paradigm. The main experimental finding in this work is that jICA revealed significantly stronger and more extensive activity in brain regions associated with the auditory P300 ERP than a P300 linear regression analysis, both at the group level and within-subject. The results suggest that, with the incorporation of spatial and temporal information from both imaging modalities, jICA is more sensitive to neural sources commonly observed with ERP and fMRI compared to a linear regression analysis. Furthermore, computational simulations suggest that jICA can extract linear and nonlinear relationships between ERP and fMRI signals, as well as uncoupled sources (i.e., sources with a signal in only one imaging modality). These features of jICA can be important for assessing disease states in which the relationship between the ERP and fMRI signals is unknown, as well as pathological conditions causing neurovascular uncoupling, such as stroke

Exposing Internal Attentional Brain States using Single-Trial EEG Analysis with Combined Imaging Modalities

The goal of this dissertation is to explore the neural correlates of endogenous task-related attentional modulations. Natural fluctuations in task engagement are challenging to study, primarily because they are by nature not event related and thus cannot be controlled experimentally. Here we exploit well-accepted links between attention and various measures of neural activity while subjects perform simple target detection tasks that leave their minds free to wander. We use multimodal neuroimaging, specifically simultaneous electroencephalograpy and functional magnetic resonance imaging (EEG-fMRI) and EEG-pupillometry, with data-driven machine learning methods and study activity across the whole brain. We investigate BOLD fMRI correlates of EEG variability spanning each trial, enabling us to unravel a cascade of attention-related activations and determine their temporal ordering. We study activity during auditory and visual paradigms independently, and we also combine data to investigate supra modal attention systems. Without aiming to study known attention-related functional brain networks, we found correlates of attentional modulations in areas representative of the default mode network (DMN), ventral attention network (VAN), locus coeruleus norepinephrine (LC-NE) system, and regions implicated in generation of the extensively-studied P300 EEG response to target stimuli. Our results reveal complex interactions between known attentional systems, and do so non-invasively to study normal fluctuations of task engagement in the human brain

Somatosensory System Deficits in Schizophrenia Revealed by MEG during a Median-Nerve Oddball Task

Although impairments related to somatosensory perception are common in schizophrenia, they have rarely been examined in functional imaging studies. In the present study, magnetoencephalography (MEG) was used to identify neural networks that support attention to somatosensory stimuli in healthy adults and abnormalities in these networks in patient with schizophrenia. A median-nerve oddball task was used to probe attention to somatosensory stimuli, and an advanced, high-resolution MEG source-imaging method was applied to assess activity throughout the brain. In nineteen healthy subjects, attention-related activation was seen in a sensorimotor network involving primary somatosensory (S1), secondary somatosensory (S2), primary motor (M1), pre-motor (PMA), and paracentral lobule (PCL) areas. A frontal–parietal–temporal “attention network”, containing dorsal- and ventral–lateral prefrontal cortex (DLPFC and VLPFC), orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), superior parietal lobule (SPL), inferior parietal lobule (IPL)/supramarginal gyrus (SMG), and temporal lobe areas, was also activated. Seventeen individuals with schizophrenia showed early attention-related hyperactivations in S1 and M1 but hypo-activation in S1, S2, M1, and PMA at later latency in the sensorimotor network. Within this attention network, hypoactivation was found in SPL, DLPFC, orbitofrontal cortex, and the dorsal aspect of ACC. Hyperactivation was seen in SMG/IPL, frontal pole, and the ventral aspect of ACC in patients. These findings link attention-related somatosensory deficits to dysfunction in both sensorimotor and frontal–parietal–temporal networks in schizophrenia

Electrophysiological underpinnings of reward processing: Are we exploiting the full potential of EEG?

Understanding how the brain processes reward is an important and complex endeavor, which has involved the use of a range of complementary neuroimaging tools, including electroencephalography (EEG). EEG has been praised for its high temporal resolution but, because the signal recorded at the scalp is a mixture of brain activities, it is often considered to have poor spatial resolution. Besides, EEG data analysis has most often relied on event-related potentials (ERPs) which cancel out non-phase locked oscillatory activity, thus limiting the functional discriminative power of EEG attainable through spectral analyses. Because these three dimensions -temporal, spatial and spectral- have been unequally leveraged in reward studies, we argue that the full potential of EEG has not been exploited. To back up our claim, we first performed a systematic survey of EEG studies assessing reward processing. Specifically, we report on the nature of the cognitive processes investigated (i.e., reward anticipation or reward outcome processing) and the methods used to collect and process the EEG data (i.e., event-related potential, time-frequency or source analyses). A total of 359 studies involving healthy subjects and the delivery of monetary rewards were surveyed. We show that reward anticipation has been overlooked (88% of studies investigated reward outcome processing, while only 24% investigated reward anticipation), and that time-frequency and source analyses (respectively reported by 19% and 12% of the studies) have not been widely adopted by the field yet, with ERPs still being the dominant methodology (92% of the studies). We argue that this focus on feedback-related ERPs provides a biased perspective on reward processing, by ignoring reward anticipation processes as well as a large part of the information contained in the EEG signal. Finally, we illustrate with selected examples how addressing these issues could benefit the field, relying on approaches combining time-frequency analyses, blind source separation and source localization

User-centered design in brain–computer interfaces — a case study

The array of available brain–computer interface (BCI) paradigms has continued to grow, and so has the corresponding set of machine learning methods which are at the core of BCI systems. The latter have evolved to provide more robust data analysis solutions, and as a consequence the proportion of healthy BCI users who can use a BCI successfully is growing. With this development the chances have increased that the needs and abilities of specific patients, the end-users, can be covered by an existing BCI approach. However, most end-users who have experienced the use of a BCI system at all have encountered a single paradigm only. This paradigm is typically the one that is being tested in the study that the end-user happens to be enrolled in, along with other end-users. Though this corresponds to the preferred study arrangement for basic research, it does not ensure that the end-user experiences a working BCI. In this study, a different approach was taken; that of a user-centered design. It is the prevailing process in traditional assistive technology. Given an individual user with a particular clinical profile, several available BCI approaches are tested and – if necessary – adapted to him/her until a suitable BCI system is found

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

Electrophysiological potentials in the hippocampus during recognition memory

Neural circuits within the medial temporal lobe, including the hippocampus, support recognition memory. Patients and animals with circumscribed hippocampus damage experience significant episodic memory deficits but their general intellectual abilities remain intact. The present thesis sought to determine whether electrical field potentials generated in the hippocampus during a recognition memory task are influenced by stimulus repetition. Electroencephalography (EEG) is a non-invasive technique used to measure the voltage changes on the scalp in regards to a specific cognitive event. Event related potentials (ERP) is a post-hoc analysis of recorded EEG and allows the voltage changes to be time-locked to the occurrence of an event. EEG/ERP analysis was used to measure the voltage change in normal, healthy college students in a recognition memory task. When visual stimuli are presented repeatedly certain ERP components that are associated with recognition memory are attenuated, although some discrepancies exist in the literature. Beamforming spatial filtering analysis is a source estimation technique that allows inferences to be made about the locations of generators of the evoked potentials that are recorded at the scalp. In the current study, a repetition effect was observed for pictures and words at select electrode sites. Pictures and words previously seen (“Old’) had significantly decreased peak amplitude compared to pictures and words that had not been seen before. Although stimulus repetition clearly attenuated electrophysiological signals recorded on certain regions of the scalp, beamforming analysis of the hippocampus showed that these changes were not accompanied by similar changes in the field potentials generated within the hippocampus. Interestingly, however, pictures and words produced markedly different hippocampal field potentials