Listening to real-world sounds: fMRI data for analyzing connectivity networks.

There is a growing interest in functional magnetic resonance imaging (fMRI) studies on connectivity networks in the brain when subjects are under exposure to natural sensory stimulation. Because of a complicated coupling between spontaneous and evoked brain activity under real-world stimulation, there is no critical mapping between the experimental inputs and corresponding brain responses. The dataset contains auditory fMRI scans and T1-weighted anatomical scans acquired under eyes-closed and eyes-open conditions.

Brain dynamics and connectivity networks under natural auditory stimulation.

The analysis of functional magnetic resonance imaging (fMRI) data is challenging when subjects are under exposure to natural sensory stimulation. In this study, a two-stage approach was developed to enable the identification of connectivity networks involved in the processing of information in the brain under natural sensory stimulation. In the first stage, the degree of concordance between the results of inter-subject and intra-subject correlation analyses is assessed statistically.

Resting heart rate variability in young women is a predictor of EEG reactions to linguistic ambiguity in sentences.

Recent research has found a relationship between heart rate variability (HRV) and cognitive control mechanisms underlying various experimental tasks. This study explored the interaction between gender and resting-state HRV in brain oscillatory activity during visual recognition of linguistic ambiguity while taking state and trait anxiety scores into account. It is well known that stress or anxiety increases arousal levels, particularly under uncertainty situations.

MR image segmentation using a power transformation approach.

This study proposes a segmentation method for brain MR images using a distribution transformation approach. The method extends traditional Gaussian mixtures expectation-maximization segmentation to a power transformed version of mixed intensity distributions, which includes Gaussian mixtures as a special case. As MR intensities tend to exhibit non-Gaussianity due to partial volume effects, the proposed method is designed to fit non-Gaussian tissue intensity distributions.

EEG-correlates of trait anxiety in the stop-signal paradigm.

The relationship between trait anxiety and event-related EEG oscillatory reactions in the stop-signal paradigm was studied in 15 non-clinical subjects with average age of 26 years (13 men). In the paradigm, subjects responded to target stimuli by pressing one of the two choice buttons. In 30 out of 130 trials, target presentation was followed by a stop-signal, indicating that subjects had to refrain from a prepared motor response.

A method for generating reproducible evidence in fMRI studies.

Insights into cognitive neuroscience from neuroimaging techniques are now required to go beyond the localisation of well-known cognitive functions. Fundamental to this is the notion of reproducibility of experimental outcomes. This paper addresses the central issue that functional magnetic resonance imaging (fMRI) experiments will produce more desirable information if researchers begin to search for reproducible evidence rather than only p value significance.

Functional MR images and scientific inference: reproducibility maps.

Historically, reproducibility has been the sine qua non of experimental findings that are considered to be scientifically useful. Typically, findings from functional magnetic resonance imaging (fMRI) studies are assessed with statistical parametric maps (SPMs) using a p value threshold. However, a smaller p value does not imply that the observed result will be reproducible.