Mal-Adaptation of Event-Related EEG Responses Preceding Performance Errors

Recent EEG and fMRI evidence suggests that behavioral errors are foreshadowed by systematic changes in brain activity preceding the outcome by seconds. In order to further characterize this type of error precursor activity, we investigated single-trial event-related EEG activity from 70 participants performing a modified Eriksen flanker task, in particular focusing on the trial-by-trial dynamics of a fronto-central independent component that previously has been associated with error and feedback processing. The stimulus-locked peaks in the N2 and P3 latency range in the event-related averages showed expected compatibility and error-related modulations. In addition, a small pre-stimulus negative slow wave was present at erroneous trials. Significant error-preceding activity was found in local stimulus sequences with decreased conflict in the form of less negativity at the N2 latency (310–350 ms) accumulating across five trials before errors; concomitantly response times were speeding across trials. These results illustrate that error-preceding activity in event-related EEG is associated with the performance monitoring system and we conclude that the dynamics of performance monitoring contribute to the generation of error-prone states in addition to the more remote and indirect effects in ongoing activity such as posterior alpha power in EEG and default mode drifts in fMRI

Development of Performance and ERPs in a Flanker Task in Children and Adolescents with Tourette Syndrome—A Follow-Up Study

Background: Tourette Syndrome (TS) is a neurodevelopmental disorder with childhood-onset, with a typical decline in tic severity, as well as an increasing ability to suppress tics in late childhood and adolescence. These processes develop in parallel with general improvement of self-regulatory abilities, and performance monitoring during this age-span. Hence, changes in performance monitoring over time might provide insight into the regulation of tics in children and adolescents with TS.Method: We measured reaction time, reaction time variability, accuracy, and event-related potentials (ERP) in 17 children with TS, including 10 children with comorbid Attention-Deficit/Hyperactivity Disorder (ADHD), 24 children with ADHD, and 29 typically developing children, using a modified Eriksen Flanker task in two testing sessions administered on average 4.5 years apart. We then compared task performance, as well as ERP components across groups, and over time using regression models.Results: Task performance improved in all groups with age, and behavioral differences between children with TS and controls diminished at second assessment, while differences between controls and children with ADHD largely persisted. In terms of ERP, the early P3 developed earlier in children with TS compared with controls at the first assessment, but trajectories converged with maturation. ERP component amplitudes correlated with worst-ever tic scores.Conclusions: Merging trajectories between children with TS and controls are consistent with the development of compensatory self-regulation mechanisms during early adolescence, probably facilitating tic suppression, in contrast to children with ADHD. Correlations between ERP amplitudes and tic scores also support this notion

EEGIFT: Group Independent Component Analysis for Event-Related EEG Data

Independent component analysis (ICA) is a powerful method for source separation and has been used for decomposition of EEG, MRI, and concurrent EEG-fMRI data. ICA is not naturally suited to draw group inferences since it is a non-trivial problem to identify and order components across individuals. One solution to this problem is to create aggregate data containing observations from all subjects, estimate a single set of components and then back-reconstruct this in the individual data. Here, we describe such a group-level temporal ICA model for event related EEG. When used for EEG time series analysis, the accuracy of component detection and back-reconstruction with a group model is dependent on the degree of intra- and interindividual time and phase-locking of event related EEG processes. We illustrate this dependency in a group analysis of hybrid data consisting of three simulated event-related sources with varying degrees of latency jitter and variable topographies. Reconstruction accuracy was tested for temporal jitter 1, 2 and 3 times the FWHM of the sources for a number of algorithms. The results indicate that group ICA is adequate for decomposition of single trials with physiological jitter, and reconstructs event related sources with high accuracy

Evaluation of evoked potentials to dyadic tones after cochlear implantation

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 implantatio

MEG and fMRI Fusion for Non-Linear Estimation of Neural and BOLD Signal Changes

The combined analysis of magnetoencephalography (MEG)/electroencephalography and functional magnetic resonance imaging (fMRI) measurements can lead to improvement in the description of the dynamical and spatial properties of brain activity. In this paper we empirically demonstrate this improvement using simulated and recorded task related MEG and fMRI activity. Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique. In synthetic data, we show that MEG and fMRI fusion improves estimation of the indirectly observed neural activity and smooths tracking of the blood oxygenation level dependent (BOLD) response. In recordings of task related neural activity the combination of MEG and fMRI produces a result with greater signal-to-noise ratio, that confirms the expectation arising from the nature of the experiment. The highly non-linear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity. We show that joint analysis of the data improves the system's behavior by stabilizing the differential equations system and by requiring fewer computational resources

Visual activation of auditory cortex reflects maladaptive plasticity in cochlear implant users

Cross-modal reorganization in the auditory cortex has been reported in deaf individuals. However, it is not well understood whether this compensatory reorganization induced by auditory deprivation recedes once the sensation of hearing is partially restored through a cochlear implant. The current study used electroencephalography source localization to examine cross-modal reorganization in the auditory cortex of post-lingually deafened cochlear implant users. We analysed visual-evoked potentials to parametrically modulated reversing chequerboard images between cochlear implant users (n = 11) and normal-hearing listeners (n = 11). The results revealed smaller P100 amplitudes and reduced visual cortex activation in cochlear implant users compared with normal-hearing listeners. At the P100 latency, cochlear implant users also showed activation in the right auditory cortex, which was inversely related to speech recognition ability with the cochlear implant. These results confirm a visual take-over in the auditory cortex of cochlear implant users. Incomplete reversal of this deafness-induced cortical reorganization might limit clinical benefit from a cochlear implant and help explain the high inter-subject variability in auditory speech comprehensio

A Baseline for the Multivariate Comparison of Resting-State Networks

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease

Electrophysiological and Hemodynamic Correlates of Expectancy in Target Processing

Identifying patterns of recurrent events is central to human perception, cognition and behavior. By extracting patterns from the environment, individuals can make efficient predictions about future events. By and large, the detection of these contingencies is the core faculty to respond to, interact with, and ultimately make sense of the world. The aim of this thesis was to investigate how the brain treats temporal patterns and generates expectancies from regular event sequences. A variant of an auditory oddball paradigm was developed in which predictability was modulated with sequences of random and regular targets. In order to assess both the temporal and spatial implementation of these effects, single trial event related potentials and functional magnetic resonance imaging were employed. In the first paper, the effect of predictability on brain activity was studied with single trial ERPs, yielding sigmoid-shaped learning curves on CNV, N2 and P3. The second paper described a method for integration of single-trial ERP with fMRI data, and reported three spatiotemporal activation patterns during the P2, N2, and P3 in addition to the generic activation elicited by target stimuli. An additional modulation beginning during the N1 was extracted in the third paper that employed a method for parallel unmixing of concurrent EEG-fMRI data. The results of the thesis have implications for the understanding of ERP components, the concepts of how a standard representation is formed and how context is updated need to take into account the effects of predictability observed here. Furthermore, the thesis presents straightforward methods for single-trial ERP, and concurrent EEG-fMRI analysis that afford comprehensive spatiotemporal mapping of event-related processes in the brain