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EEG signals recorded simultaneously with fMRI are massively compromised by severe artefacts, among them the cardiac cycle-related ballistocardiogram (BCG) artefact. Different methods have been proposed to remove the BCG artefact focusing on channel-wise template subtraction procedures or spatial filtering approaches such as independent component analysis (ICA). Here we systematically compared the performance of the optimal basis set (OBS), a channel-wise correction approach, with ICA and a recently proposed combination of both (OBS-ICA). The three different procedures were applied to 60-channel EEG data from 12 subjects recorded during fMRI acquisition in a 3-T scanner. In addition to examination of the residual BCG artefact, the signal-to-noise ratio (SNR) and the topography of the resulting auditory evoked potential component NI were compared. Whereas all three approaches led to a significant artefact reduction, the ICA procedure resulted in a significantly reduced NI SNR and amplitude when compared to BCG-uncorrected data, indicating a rather poor performance. In contrast to ICA, OBS and OBS-ICA corrected data substantially improved the SNR of the N1. The quality of the auditory evoked potential NI topography was investigated by means of equivalent current dipole modelling. On a descriptive level, all three correction procedures led to a reduced localization error when compared to BCG-uncorrected data. This improvement was significant for OBS-ICA. We conclude that OBS and OBS-ICA can efficiently remove BCG artefacts and substantially improve the quality of EEG signals recorded inside the scanner, a prerequisite for the successful integration of simultaneously recorded EEG and IMR].

Item does not contain fulltext, One of the major challenges in science is to study and understand the human brain. Numerous methods examining different aspects of brain functions have been developed and employed. To study systemic interactions brain networks in vivo, non-invasive methods such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have been used with great success. However, each of these methods can map only certain, quite selective aspects of brain function while missing others, and the inferences on neuronal processes and information flow are often rather indirect. To overcome these shortcomings of single methods, researchers have attempted to combine methods in order to make optimal use of their advantages while compensating their disadvantages. Hence, it is not surprising that soon after the introduction of fMRI as a neuroimaging method the possibilities of combinations with EEG have been explored. This book is intended to aid researchers who plan to set up a simultaneous EEG-fMRI laboratory and those who are interested in integrating electrophysiological and hemodynamic data. As will be obvious from the different chapters, this is a dynamically developing field in which several approaches are being tested, validated and compared. Currently, there is no one best solution for all problems available, but many promising techniques are emerging. This book shall give a comprehensive overview of these techniques. In addition, it points to open questions and directions for future research.

Auditory evoked potentials are tools widely used to assess auditory cortex functions in clinical context. However, in cochlear implant users, electrophysiological measures are challenging due to implant-created artefacts in the EEG. Here, we used independent component analysis to reduce cochlear implant-related artefacts in event-related EEGs of cochlear implant users (n = 12), which allowed detailed spatio-temporal evaluation of auditory evoked potentials by means of dipole source analysis. The present study examined hemispheric asymmetries of auditory evoked potentials to musical sounds in cochlear implant users to evaluate the effect of this type of implantation on neuronal activity. In particular, implant users were presented with two dyadic tonal intervals in an active oddball design and in a passive listening condition. Principally, the results show that independent component analysis is an efficient approach that enables the study of neurophysiological mechanisms of restored auditory function in cochlear implant users. Moreover, our data indicate altered hemispheric asymmetries for dyadic tone processing in implant users compared with listeners with normal hearing (n = 12). We conclude that the evaluation of auditory evoked potentials are of major relevance to understanding auditory cortex function after cochlear implantation and could be of substantial clinical value by indicating the maturation/reorganization of the auditory system after implantation.

In this review we are concerned specifically with the putative role of the default-mode network (DMN) in the pathophysiology of mental disorders. First, we define the DMN concept with regard to its neuro- anatomy, its functional organisation through low frequency neuronal oscillations, its relation to other recently discovered low frequency resting state networks, and the cognitive functions it is thought to serve. Second, we introduce methodological and analytical issues and challenges. Third, we describe putative mechanisms proposed to link DMN abnormalities and mental disorders. These include interference by network activity during task performance, altered patterns of antagonism between task specific and non-specific elements, altered connectively and integrity of the DMN, and altered psychological functions served by the network DMN. Fourth, we review the empirical literature systematically. We relate DMN dysfunction to dementia,schizophrenia,epilepsy,anxiety and depression, autism and attention deficit/hyperactivity disorder drawing out common and unique elements of the disorders. Finally, we provide an integrative overview and highlight important challenges and tasks for future research.

Recent evidence suggests that both spatiotemporally distinct and overlapping brain regions are involved in bottom-up- and top-down-driven attentional processing. However, existing studies are based on a variety of different approaches, including electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), raising the question of how EEG and fMRI findings in this field are related to each other. The present study aimed at disentangling common from specific regions underlying bottom-up novelty-processing and top-down target-processing. Simultaneous EEG and fMRI recordings were employed to investigate how fMRI-identified brain regions contribute to event-related potential (ERP) signatures of novelty- and target-processing. Fourteen subjects performed a modified novelty oddball task in which either rare tones or novel sounds served as targets in different blocks, allowing us to separate novelty-related from mere distractor-related effects. ERP signatures of novelty- and target-processing could be identified, confirming previous research based on recordings outside the scanner. fMRI analyses revealed that, despite considerable overlap of regions activated during novelty- and target-processing, bilateral superior temporal and right inferior frontal areas showed pronounced activation related to novelty-processing. fMRI-informed ERP dipole seeding was used to integrate both signals. The source modeling results further implicated temporal and inferior frontal sources in novelty-processing. Target-related fMRI activation on the other hand was confirmed in a network comprising distributed frontoparietal regions as well as bilateral caudate nucleus and cerebellum. Most regions identified by fMRI showed a contribution to target-related ERP signatures. This pattern of findings underscores the potential of simultaneous EEG/fMRI recordings for the spatiotemporal characterization of target- and novelty-processing.

Contains fulltext : 148954.pdf (publisher's version ) (Open Access), Humans engaged in monotonous tasks are susceptible to occasional errors that may lead to serious consequences, but little is known about brain activity patterns preceding errors. Using functional Mill and applying independent component analysis followed by deconvolution of hemodynamic responses, we studied error preceding brain activity on a trial-by-trial basis. We found a set of brain regions in which the temporal evolution of activation predicted performance errors. These maladaptive brain activity changes started to evolve approximate to 30 sec before the error. In particular, a coincident decrease of deactivation in default mode regions of the brain, together with a decline of activation in regions associated with maintaining task effort, raised the probability of future errors. Our findings provide insights into the brain network dynamics preceding human performance errors and suggest that monitoring of the identified precursor states may help in avoiding human errors in critical real-world situations.

Contains fulltext : 122315.pdf (publisher's version ) (Open Access), The P300 component of the human event-related brain potential has often been linked to the processing of rare, surprising events. However, the formal computational processes underlying the generation of the P300 are not well known. Here, we formulate a simple model of trial-by-trial learning of stimulus probabilities based on Information Theory. Specifically, we modeled the surprise associated with the occurrence of a visual stimulus to provide a formal quantification of the "subjective probability" associated with an event. Subjects performed a choice reaction time task, while we recorded their brain responses using electroencephalography (EEG). In each of 12 blocks, the probabilities of stimulus occurrence were changed, thereby creating sequences of trials with low, medium, and high predictability. Trial-by-trial variations in the P300 component were best explained by a model of stimulus-bound surprise. This model accounted for the data better than a categorical model that parametrically encoded the stimulus identity, or an alternative model of surprise based on the Kullback-Leibler divergence. The present data demonstrate that trial-by-trial changes in P300 can be explained by predictions made by an ideal observer keeping track of the probabilities of possible events. This provides evidence for theories proposing a direct link between the P300 component and the processing of surprising events. Furthermore, this study demonstrates how model-based analyses can be used to explain significant proportions of the trial-by-trial changes in human event-related EEG responses.