Patricia Wollstadt

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Joseph T. Lizier17
Albert Y. Zomaya15
Michael Wibral14
Robert Oostenveld12
Holger Kantz12
12Michael Wibral
5Ryan A Stevenson
5Mark T Wallace
5Sarah H Baum
4Luca Faes

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2012
2017
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Brain Region

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Reduced predictable information in brain signals in autism spectrum disorder
Autism spectrum disorder (ASD) is a common developmental disorder characterized by communication difficulties and impaired social interaction. Recent results suggest altered brain dynamics as a potential cause of symptoms in ASD. Here, we aim to describe potential information-processing consequences of these alterations by measuring active information… (More)
Efficient Transfer Entropy Analysis of Non-Stationary Neural Time Series
Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of information transfer, transfer entropy, has seen a dramatic surge of interest in neuroscience. Estimating transfer entropy from two processes requires the observation of multiple… (More)
A Graph Algorithmic Approach to Separate Direct from Indirect Neural Interactions
Network graphs have become a popular tool to represent complex systems composed of many interacting subunits; especially in neuroscience, network graphs are increasingly used to represent and analyze functional interactions between multiple neural sources. Interactions are often reconstructed using pairwise bivariate analyses, overlooking the multivariate… (More)
Revisiting Wiener's principle of causality -- interaction-delay reconstruction using transfer entropy and multivariate analysis on delay-weighted graphs.
To understand the function of networks we have to identify the structure of their interactions, but also interaction timing, as compromised timing of interactions may disrupt network function. We demonstrate how both questions can be addressed using a modified estimator of transfer entropy. Transfer entropy is an implementation of Wiener's principle of… (More)
Anesthesia-related changes in information transfer may be caused by reduction in local information generation
In anesthesia research it is an open question how general anesthetics lead to loss of consciousness (LOC). It has been proposed that LOC may be caused by the disruption of cortical information processing, preventing information integration. Therefore, recent studies investigating information processing under anesthesia focused on changes in information… (More)
Breakdown of local information processing may underlie isoflurane anesthesia effects
The disruption of coupling between brain areas has been suggested as the mechanism underlying loss of consciousness in anesthesia. This hypothesis has been tested previously by measuring the information transfer between brain areas, and by taking reduced information transfer as a proxy for decoupling. Yet, information transfer is a function of the amount of… (More)
Need for and challenges facing functional communication as outcome parameter in AD clinical trials.
This paper (1) highlights the relevance of functional communication as an outcome parameter in Alzheimer disease (AD) clinical trials; (2) identifies studies that have reported functional communication outcome measures in AD clinical trials; (3) critically reviews the scales of functional communication used in recent AD clinical trials by summarizing the… (More)
Information theoretic evidence for predictive coding in the face processing system.
Predictive coding suggests that the brain infers the causes of its sensations by combining sensory evidence with internal predictions based on available prior knowledge. However, the neurophysiological correlates of (pre-)activated prior knowledge serving these predictions are still unknown. Based on the idea that such pre-activated prior knowledge must be… (More)
Graphical analyses in delay interaction networks
Network or graph theory has become a popular tool to represent and analyze large-scale interaction patterns in the brain. To derive a functional network representation from experimentally recorded neural time series one has to identify the structure of the interactions between these time series. In neuroscience, this is often done by pairwise bivariate… (More)
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