Use It or Lose It: Facilitating the Use of Interactive Data Apps in Psychological Research Data Sharing.

The value of open research data (ORD), a key feature of open science, lies in their reuse. However, the mere online availability of ORD does not guarantee their reuse by other researchers. Specifically, previous meta-scientific research has indicated that the underutilization of ORD is related to barriers at the level of the ORD themselves, potential reusers of ORD, and the broader academic ecosystem.

Human Belief State-Based Exploration and Exploitation in an Information-Selective Symmetric Reversal Bandit Task.

Unlabelled: Humans often face sequential decision-making problems, in which information about the environmental reward structure is detached from rewards for a subset of actions. In the current exploratory study, we introduce an information-selective symmetric reversal bandit task to model such situations and obtained choice data on this task from 24 participants. To arbitrate between different decision-making strategies that participants may use on this task, we developed a set of probabilistic agent-based behavioral models, including exploitative and explorative Bayesian agents, as well as heuristic control agents.

Neural surprise in somatosensory Bayesian learning.

Tracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles. However, much remains unknown about the employed algorithms, for somesthesis in particular.

Brain-Behavior Associations for Risk Taking Depend on the Measures Used to Capture Individual Differences.

Maladaptive risk taking can have severe individual and societal consequences; thus, individual differences are prominent targets for intervention and prevention. Although brain activation has been shown to be associated with individual differences in risk taking, the directionality of the reported brain-behavior associations is less clear. Here, we argue that one aspect contributing to the mixed results is the low convergence between risk-taking measures, especially between the behavioral tasks used to elicit neural functional markers.

Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple-acquisition MRI.

Fractal analysis represents a promising new approach to structural neuroimaging data, yet systematic evaluation of the fractal dimension (FD) as a marker of structural brain complexity is scarce. Here we present in-depth methodological assessment of FD estimation in structural brain MRI. On the computational side, we show that spatial scale optimization can significantly improve FD estimation accuracy, as suggested by simulation studies with known FD values.

Digital open science-Teaching digital tools for reproducible and transparent research.

An important hallmark of science is the transparency and reproducibility of scientific results. Over the last few years, internet-based technologies have emerged that allow for a representation of the scientific process that goes far beyond traditional methods and analysis descriptions. Using these often freely available tools requires a suite of skills that is not necessarily part of a curriculum in the life sciences.

Computing integrated information.

Integrated information theory (IIT) has established itself as one of the leading theories for the study of consciousness. IIT essentially proposes that quantitative consciousness is identical to maximally integrated conceptual information, quantified by a measure called Φ, and that phenomenological experience corresponds to the associated set of maximally irreducible cause-effect repertoires of a physical system being in a certain state. With the current work, we provide a general formulation of the framework, which comprehensively and parsimoniously expresses Φ in the language of probabilistic models.

Variational Bayesian Parameter Estimation Techniques for the General Linear Model.

Variational Bayes (VB), variational maximum likelihood (VML), restricted maximum likelihood (ReML), and maximum likelihood (ML) are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data. However, the theoretical underpinnings of these model parameter estimation techniques are rarely covered in introductory statistical texts. Because of the widespread practical use of VB, VML, ReML, and ML in the neuroimaging community, we reasoned that a theoretical treatment of their relationships and their application in a basic modeling scenario may be helpful for both neuroimaging novices and practitioners alike.

Probabilistic delay differential equation modeling of event-related potentials.

"Dynamic causal models" (DCMs) are a promising approach in the analysis of functional neuroimaging data due to their biophysical interpretability and their consolidation of functional-segregative and functional-integrative propositions. In this theoretical note we are concerned with the DCM framework for electroencephalographically recorded event-related potentials (ERP-DCM). Intuitively, ERP-DCM combines deterministic dynamical neural mass models with dipole-based EEG forward models to describe the event-related scalp potential time-series over the entire electrode space.

A normative inference approach for optimal sample sizes in decisions from experience.

"Decisions from experience" (DFE) refers to a body of work that emerged in research on behavioral decision making over the last decade. One of the major experimental paradigms employed to study experience-based choice is the "sampling paradigm," which serves as a model of decision making under limited knowledge about the statistical structure of the world. In this paradigm respondents are presented with two payoff distributions, which, in contrast to standard approaches in behavioral economics, are specified not in terms of explicit outcome-probability information, but by the opportunity to sample outcomes from each distribution without economic consequences.

Early haemodynamic changes observed in patients with epilepsy, in a visual experiment and in simulations.

Objective: The objective of this study was to investigate whether previously reported early blood oxygen level dependent (BOLD) changes in epilepsy could occur as a result of the modelling techniques rather than physiological changes.

Methods: EEG-fMRI data were analysed from seven patients with focal epilepsy, six control subjects undergoing a visual experiment, in addition to simulations. In six separate analyses the event timing was shifted by either -9,-6,-3,+3,+6 or +9 s relative to the onset of the interictal epileptiform discharge (IED) or stimulus.

Reliability of information-based integration of EEG and fMRI data: a simulation study.

Most studies involving simultaneous electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data rely on the first-order, affine-linear correlation of EEG and fMRI features within the framework of the general linear model. An alternative is the use of information-based measures such as mutual information and entropy, which can also detect higher-order correlations present in the data. The estimate of information-theoretic quantities might be influenced by several parameters, such as the numerosity of the sample, the amount of correlation between variables, and the discretization (or binning) strategy of choice.

Imaging tactile imagery: changes in brain connectivity support perceptual grounding of mental images in primary sensory cortices.

Constructing mental representations in the absence of sensory stimulation is a fundamental ability of the human mind and has been investigated in numerous brain imaging studies. However, it is still unclear how brain areas facilitating mental construction processes interact with brain regions related to specific sensory representations. In this fMRI study subjects formed mental representations of tactile stimuli either from memory (imagery) or from presentation of actual corresponding vibrotactile patterned stimuli.

The structural-functional connectome and the default mode network of the human brain.

An emerging field of human brain imaging deals with the characterization of the connectome, a comprehensive global description of structural and functional connectivity within the human brain. However, the question of how functional and structural connectivity are related has not been fully answered yet. Here, we used different methods to estimate the connectivity between each voxel of the cerebral cortex based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data in order to obtain observer-independent functional-structural connectomes of the human brain.

Spontaneous EEG alpha oscillation interacts with positive and negative BOLD responses in the visual-auditory cortices and default-mode network.

The human brain is continually, dynamically active and spontaneous fluctuations in this activity play a functional role in affecting both behavioural and neuronal responses. However, the mechanisms through which this occurs remain poorly understood. Simultaneous EEG-fMRI is a promising technique to study how spontaneous activity modulates the brain's response to stimulation, as temporal indices of ongoing cortical excitability can be integrated with spatially localised evoked responses.

Evidence for neural encoding of Bayesian surprise in human somatosensation.

Accumulating empirical evidence suggests a role of Bayesian inference and learning for shaping neural responses in auditory and visual perception. However, its relevance for somatosensory processing is unclear. In the present study we test the hypothesis that cortical somatosensory processing exhibits dynamics that are consistent with Bayesian accounts of brain function.

EEG-fMRI based information theoretic characterization of the human perceptual decision system.

The modern metaphor of the brain is that of a dynamic information processing device. In the current study we investigate how a core cognitive network of the human brain, the perceptual decision system, can be characterized regarding its spatiotemporal representation of task-relevant information. We capitalize on a recently developed information theoretic framework for the analysis of simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging data (fMRI) (Ostwald et al.

Multimodal functional network connectivity: an EEG-fMRI fusion in network space.

EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space.

Information theoretic approaches to functional neuroimaging.

Information theory is a probabilistic framework that allows the quantification of statistical non-independence between signals of interest. In contrast to other methods used for this purpose, it is model free, i.e.

The relationship between the visual evoked potential and the gamma band investigated by blind and semi-blind methods.

Gamma Band Activity (GBA) is increasingly studied for its relation with attention, change detection, maintenance of working memory and the processing of sensory stimuli. Activity around the gamma range has also been linked with early visual processing, although the relationship between this activity and the low frequency visual evoked potential (VEP) remains unclear. This study examined the ability of blind and semi-blind source separation techniques to extract sources specifically related to the VEP and GBA in order to shed light on the relationship between them.

Voxel-wise information theoretic EEG-fMRI feature integration.

We have recently proposed the evaluation of a set of information theoretic quantities (ITQs) for the integration of simultaneously acquired EEG-fMRI data (Ostwald, D., Porcaro, C., Bagshaw, A.

Perceptual decisions formed by accumulation of audiovisual evidence in prefrontal cortex.

To form perceptual decisions in our multisensory environment, the brain needs to integrate sensory information derived from a common source and segregate information emanating from different sources. Combining fMRI and psychophysics in humans, we investigated how the brain accumulates sensory evidence about a visual source in the context of congruent or conflicting auditory information. In a visual selective attention paradigm, subjects (12 females, 7 males) categorized video clips while ignoring concurrent congruent or incongruent soundtracks.

Cortical processing of near-threshold tactile stimuli: an MEG study.

In the present study we tested the applicability of a paired-stimulus paradigm for the investigation of near-threshold (NT) stimulus processing in the somatosensory system using magnetoencephalography. Cortical processing of the NT stimuli was studied indirectly by investigating the impact of NT stimuli on the source activity of succeeding suprathreshold test stimuli. We hypothesized that cortical responses evoked by test stimuli are reduced due to the preactivation of the same finger representation by the preceding NT stimulus.

Functional source separation improves the quality of single trial visual evoked potentials recorded during concurrent EEG-fMRI.

EEG quality is a crucial issue when acquiring combined EEG-fMRI data, particularly when the focus is on using single trial (ST) variability to integrate the data sets. The most common method for improving EEG data quality following removal of gross MRI artefacts is independent component analysis (ICA), a completely blind source separation technique. In the current study, a different approach is proposed based on the functional source separation (FSS) algorithm.