Forecasting the duration of emotions: A motivational account and self-other differences.

This research investigates the forecasts that people make about the duration of positive versus negative emotions, and tests whether these forecasts differ for self versus for others. Consistent with a motivated thinking framework, six studies show that people make optimistic, asymmetric forecasts that positive emotions will last longer than negative ones. However, for other people, wishful thinking is absent, and therefore people make less optimistic, more symmetric forecasts.

Evaluating resective surgery targets in epilepsy patients: A comparison of quantitative EEG methods.

Background: Quantitative analysis of intracranial EEG is a promising tool to assist clinicians in the planning of resective brain surgery in patients suffering from pharmacoresistant epilepsies. Quantifying the accuracy of such tools, however, is nontrivial as a ground truth to verify predictions about hypothetical resections is missing.

New Method: As one possibility to address this, we use customized hypotheses tests to examine the agreement of the methods on a common set of patients.

EEG synchronization measures are early outcome predictors in comatose patients after cardiac arrest.

Objective: Outcome prognostication in comatose patients after cardiac arrest (CA) remains a major challenge. Here we investigated the prognostic value of combinations of linear and non-linear bivariate EEG synchronization measures.

Methods: 94 comatose patients with EEG within 24h after CA were included.

Predictive modeling of EEG time series for evaluating surgery targets in epilepsy patients.

During the last 20 years, predictive modeling in epilepsy research has largely been concerned with the prediction of seizure events, whereas the inference of effective brain targets for resective surgery has received surprisingly little attention. In this exploratory pilot study, we describe a distributional clustering framework for the modeling of multivariate time series and use it to predict the effects of brain surgery in epilepsy patients. By analyzing the intracranial EEG, we demonstrate how patients who became seizure free after surgery are clearly distinguished from those who did not.

Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone.

Objective: To show that time-irreversible EEG signals recorded with intracranial electrodes during seizures can serve as markers of the epileptogenic zone.

Methods: We use the recently developed method of mapping time series into directed horizontal graphs (dHVG). Each node of the dHVG represents a time point in the original intracranial EEG (iEEG) signal.

Motivated Implicit Theories of Personality: My Weaknesses Will Go Away, but My Strengths Are Here to Stay.

Across six studies, this research found consistent evidence for motivated implicit theories about personality malleability: People perceive their weaknesses as more malleable than their strengths. Moreover, motivation also influences how people see themselves in the future, such that they expect their present strengths to remain constant, but they expect their present weaknesses to improve in the future. Several additional findings suggest the motivational nature of these effects: The difference in perceived malleability for strengths versus weaknesses was only observed for the self, not for other people.

Prognostic and diagnostic value of EEG signal coupling measures in coma.

Objective: Our aim was to assess the diagnostic and predictive value of several quantitative EEG (qEEG) analysis methods in comatose patients.

Methods: In 79 patients, coupling between EEG signals on the left-right (inter-hemispheric) axis and on the anterior-posterior (intra-hemispheric) axis was measured with four synchronization measures: relative delta power asymmetry, cross-correlation, symbolic mutual information and transfer entropy directionality. Results were compared with etiology of coma and clinical outcome.

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States.

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon's implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states.

Chow-Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series.

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20-30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool.

Seizure termination.

A better understanding of the mechanisms by which most focal epileptic seizures stop spontaneously within a few minutes would be of highest importance, because they could potentially help to improve existing and develop novel therapeutic measures for seizure control. Studies devoted to unraveling mechanisms of seizure termination often take one of the two following approaches. The first approach focuses on metabolic mechanisms such as ionic concentrations, acidity, or neuromodulator release, studying how they are dependent on, and in turn affect changes of neuronal activity.

Spike-based probabilistic inference in analog graphical models using interspike-interval coding.

Temporal spike codes play a crucial role in neural information processing. In particular, there is strong experimental evidence that interspike intervals (ISIs) are used for stimulus representation in neural systems. However, very few algorithmic principles exploit the benefits of such temporal codes for probabilistic inference of stimuli or decisions.

Belief propagation in networks of spiking neurons.

From a theoretical point of view, statistical inference is an attractive model of brain operation. However, it is unclear how to implement these inferential processes in neuronal networks. We offer a solution to this problem by showing in detailed simulations how the belief propagation algorithm on a factor graph can be embedded in a network of spiking neurons.