A tutorial on data-driven methods for statistically assessing ERP topographies

Dynamic changes in ERP topographies can be conveniently analyzed by means of microstates, the so-called "atoms of thoughts", that represent brief periods of quasi-stable synchronized network activation. Comparing temporal microstate features such as on- and offset or duration between groups and conditions therefore allows a precise assessment of the timing of cognitive processes. So far, this has been achieved by assigning the individual time-varying ERP maps to spatially defined microstate templates obtained from clustering the grand mean data into predetermined numbers of topographies (microstate prototypes). Features obtained from these individual assignments were then statistically compared. This has the problem that the individual noise dilutes the match between individual topographies and templates leading to lower statistical power. We therefore propose a randomization-based procedure that works without assigning grand-mean microstate prototypes to individual data. In addition, we propose a new criterion to select the optimal number of microstate prototypes based on cross-validation across subjects. After a formal introduction, the method is applied to a sample data set of an N400 experiment and to simulated data with varying signal-to-noise ratios, and the results are compared to existing methods. In a first comparison with previously employed statistical procedures, the new method showed an increased robustness to noise, and a higher sensitivity for more subtle effects of microstate timing. We conclude that the proposed method is well-suited for the assessment of timing differences in cognitive processes. The increased statistical power allows identifying more subtle effects, which is particularly important in small and scarce patient populations.

The bivalency effect represents an interference-triggered adjustment of cognitive control: an ERP study

When bivalent stimuli (i.e., stimuli with relevant features for two different tasks) occur occasionally among univalent stimuli, performance is slowed on subsequent univalent stimuli even if they have no overlapping stimulus features. This finding has been labeled the bivalency effect. It indexes an adjustment of cognitive control, but the underlying mechanism is not well understood yet. The purpose of the present study was to shed light on this question, using event-related potentials. We used a paradigm requiring predictable alternations between three tasks, with bivalent stimuli occasionally occurring on one task. The results revealed that the bivalency effect elicited a sustained parietal positivity and a frontal negativity, a neural signature that is typical for control processes implemented to resolve interference. We suggest that the bivalency effect reflects interference, which may be caused by episodic context binding.

A Team-Oriented Investigation of ERP Post-Implementation Integration Projects: How Cross-Functional Collaboration Influences ERP Benefits

The benefits companies achieve by implementing an ERP system vary considerably. Many companies need to adapt their ERP integration solution in the post-implementation stage. But after the completion of such a usually very complex integration project, benefits do not emerge by all means. A misfit between the organization and the IS, especially the aspect of cross-functional team collaboration, could explain these divergences. Using an initial theoretical framework, we conducted a single case study to explore the team-oriented perceptions in a post-implementation ERP integration project. To analyze the benefits and the influences in greater depth we disentangled the integration benefits into their particular parts (process, system and information quality). Our findings show that post-implementation ERP integration changes are not always perceived as beneficiary by the involved teams and that cross-functional collaboration has an important influence.

Improved detection of amnestic MCI by means of discriminative vector quantization of single-trial cognitive ERP responses

Cognitive event-related potentials (ERPs) are widely employed in the study of dementive disorders. The morphology of averaged response is known to be under the influence of neurodegenerative processes and exploited for diagnostic purposes. This work is built over the idea that there is additional information in the dynamics of single-trial responses. We introduce a novel way to detect mild cognitive impairment (MCI) from the recordings of auditory ERP responses. Using single trial responses from a cohort of 25 amnestic MCI patients and a group of age-matched controls, we suggest a descriptor capable of encapsulating single-trial (ST) response dynamics for the benefit of early diagnosis. A customized vector quantization (VQ) scheme is first employed to summarize the overall set of ST-responses by means of a small-sized codebook of brain waves that is semantically organized. Each ST-response is then treated as a trajectory that can be encoded as a sequence of code vectors. A subject's set of responses is consequently represented as a histogram of activated code vectors. Discriminating MCI patients from healthy controls is based on the deduced response profiles and carried out by means of a standard machine learning procedure. The novel response representation was found to improve significantly MCI detection with respect to the standard alternative representation obtained via ensemble averaging (13% in terms of sensitivity and 6% in terms of specificity). Hence, the role of cognitive ERPs as biomarker for MCI can be enhanced by adopting the delicate description of our VQ scheme.