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Abstract Background Loss of CFTR-dependent anion and fluid secretion in the ducts of the exocrine pancreas is thought to contribute to the development of pancreatitis, but little is known about the impact of inflammation on ductal CFTR function. Here we used adult stem cell-derived cell cultures (organoids) obtained from porcine pancreas to evaluate the effects of pro-inflammatory cytokines on CFTR function. Methods Organoids were cultured from porcine pancreas and used to prepare ductal epithelial monolayers. Monolayers were characterized by immunocytochemistry. Epithelial bicarbonate and chloride secretion, and the effect of IL-1β, IL-6, IFN-γ, and TNF-α on CFTR function was assessed by electrophysiology. Results Immunolocalization of ductal markers, including CFTR, keratin 7, and zonula occludens 1, demonstrated that organoid-derived cells formed a highly polarized epithelium. Stimulation by secretin or VIP triggered CFTR-dependent anion secretion across epithelial monolayers, whereas purinergic receptor stimulation by UTP, elicited CFTR-independent anion secretion. Most of the anion secretory response was attributable to bicarbonate transport. The combination of IL-1β, IL-6, IFN-γ, and TNF-α markedly enhanced CFTR expression and anion secretion across ductal epithelial monolayers, whereas these cytokines had little effect when tested separately. Although TNF-α triggered apoptotic signaling, epithelial barrier function was not significantly affected by cytokine exposure. Conclusions Pro-inflammatory cytokines enhance CFTR-dependent anion secretion across pancreatic ductal epithelium. We propose that up-regulation of CFTR in the early stages of the inflammatory response, may serve to promote the removal of pathogenic stimuli from the ductal tree, and limit tissue injury.

Abstract In the Netherlands, newborns are referred by the newborn screening (NBS) Program when a low free carnitine (C0) concentration (

Abstract Memory, one of the hallmarks of human cognition, can be modified when humans voluntarily modulate neural population activity using neurofeedback. However, it is currently unknown whether neurofeedback can influence the integration of memories, and whether memory is facilitated or impaired after such neural perturbation. In this study, participants memorized objects while we provided them with abstract neurofeedback based on their brain activity patterns in the ventral visual stream. This neurofeedback created an implicit face or house context in the brain while memorizing the objects. The results revealed that participants created associations between each memorized object and its implicit context solely due to the neurofeedback manipulation. Our findings shed light onto how memory formation can be influenced by synthetic memory tags with neurofeedback and advance our understanding of mnemonic processing.

The present study aims to connect the psychophysical research on the human visual perception of flicker with the neurophysiological research on steady-state visual evoked potentials (SSVEPs) in the context of their application needs and current technological developments. In four experiments, we investigated whether a temporal contrast sensitivity model could be established based on the electrophysiological responses to repetitive visual stimulation and, if so, how this model compares to the psychophysical models of flicker visibility. We used data from 62 observers viewing periodic flicker at a range of frequencies and modulation depths sampled around the perceptual visibility thresholds. The resulting temporal contrast sensitivity curve (TCSC) was similar in shape to its psychophysical counterpart, confirming that the human visual system is most sensitive to repetitive visual stimulation at frequencies between 10 and 20 Hz. The electrophysiological TCSC, however, was below the psychophysical TCSC measured in our experiments for lower frequencies (1–50 Hz), crossed it when the frequency was 50 Hz, and stayed above while decreasing at a slower rate for frequencies in the gamma range (40–60 Hz). This finding provides evidence that SSVEPs could be measured even without the conscious perception of flicker, particularly at frequencies above 50 Hz. The cortical and perceptual mechanisms that apply at higher temporal frequencies, however, do not seem to directly translate to lower frequencies. The presence of harmonics, which show better response for many frequencies, suggests non-linear processing in the visual system. These findings are important for the potential applications of SSVEPs in studying, assisting, or augmenting human cognitive and sensorimotor functions.

Abstract Steady-state visual evoked potentials (SSVEPs), the brain response to visual flicker stimulation, have proven beneficial in both research and clinical applications. Despite the practical advantages of stimulation at high frequencies in terms of visual comfort and safety, high frequency-SSVEPs have not received enough attention and little is known about the mechanisms behind their generation and propagation in time and space. In this study, we investigated the origin and propagation of SSVEPs in the gamma frequency band (40–60 Hz) by studying the dynamic properties of EEG in 32 subjects. Using low-resolution brain electromagnetic tomography (sLORETA) we identified the cortical sources involved in SSVEP generation in that frequency range to be in the primary visual cortex, Brodmann areas 17, 18 and 19 with minor contribution from sources in central and frontal sites. We investigated the SSVEP propagation as measured on the scalp in the framework of the existing theories regarding the neurophysiological mechanism through which the SSVEP spreads through the cortex. We found a progressive phase shift from posterior parieto-occipital sites over the cortex with a phase velocity of approx. 8–14 m/s and wavelength of about 21 and 24 cm. The SSVEP spatial properties appear sensitive to input frequency with higher stimulation frequencies showing a faster propagation speed.

Abstract Background Free carnitine has been measured in the Dutch newborn screening (NBS) program since 2007 with a referral threshold of ≤5 μmol/L, regardless of gestational age or birthweight. However, several studies suggest that carnitine concentrations may depend on gestational age and birthweight. We evaluated differences in postnatal day‐to‐day carnitine concentrations in newborns based on gestational age (GA) and/or weight for GA (WfGA). Methods A retrospective study was performed using data from the Dutch NBS. Dried blood spot (DBS) carnitine concentrations, collected between the 3rd and 10th day of life, of nearly 2 million newborns were included. Individuals were grouped based on GA and WfGA. Median carnitine concentrations were calculated for each group. Mann‐Whitney U tests, and chi‐square tests were applied to test for significant differences between groups. Results Preterm, postterm, and small for GA (SGA) newborns have higher carnitine concentrations at the third day of life compared to term newborns. The median carnitine concentration of preterm newborns declines from day 3 onwards, and approximates that of term newborns at the sixth day of life, while median concentrations of postterm and SGA newborns remain elevated at least throughout the first 10 days of life. Carnitine concentrations ≤5 μmol/L were found less frequently in SGA newborns and newborns born between 32 and 37 weeks of gestation, compared to term newborns. Conclusions Median carnitine concentrations in NBS DBS vary with day of sampling, GA, and WfGA. It is important to take these variables into account when interpreting NBS results..

Four real-time visual feedback computer tools for singing lessons (Singad, Albert, Sing & See, and WinSINGAD), and the research carried out to evaluate the usefulness of these systems are reviewed in this article. We report on the development of user-functions and the usability of these computer-assisted learning tools. Both quantitative and qualitative studies confirm the efficiency of real-time visual feedback in improving singing abilities. Having addressed these findings, we suggest further quantitative investigations of (1) the detailed effect of visual feedback on performance accuracy and on the learning process, and (2) the interactions between improvement of musical performance and the type of visual feedback and the amount of information it presents, the skill level of the user and the teacher's role.

Brain-Computer Interfaces (BCIs) allow users to control devices and communicate by using brain activity only. BCIs based on broad-band visual stimulation can outperform BCIs using other stimulation paradigms. Visual stimulation with pseudo-random bit-sequences evokes specific Broad-Band Visually Evoked Potentials (BBVEPs) that can be reliably used in BCI for high-speed communication in speller applications. In this study, we report a novel paradigm for a BBVEP-based BCI that utilizes a generative framework to predict responses to broad-band stimulation sequences. In this study we designed a BBVEP-based BCI using modulated Gold codes to mark cells in a visual speller BCI. We defined a linear generative model that decomposes full responses into overlapping single-flash responses. These single-flash responses are used to predict responses to novel stimulation sequences, which in turn serve as templates for classification. The linear generative model explains on average 50% and up to 66% of the variance of responses to both seen and unseen sequences. In an online experiment, 12 participants tested a 6 × 6 matrix speller BCI. On average, an online accuracy of 86% was reached with trial lengths of 3.21 seconds. This corresponds to an Information Transfer Rate of 48 bits per minute (approximately 9 symbols per minute). This study indicates the potential to model and predict responses to broad-band stimulation. These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only.

Locomotor malfunction represents a major problem in some neurological disorders like stroke and spinal cord injury. Robot-assisted walking devices have been used during rehabilitation of patients with these ailments for regaining and improving walking ability. Previous studies showed the advantage of brain-computer interface (BCI) based robot-assisted training combined with physical therapy in the rehabilitation of the upper limb after stroke. Therefore, stroke patients with walking disorders might also benefit from using BCI robot-assisted training protocols. In order to develop such BCI, it is necessary to evaluate the feasibility to decode walking intention from cortical patterns during robot-assisted gait training. Spectral patterns in the electroencephalogram (EEG) related to robot-assisted active and passive walking were investigated in 10 healthy volunteers (mean age 32.3±10.8, six female) and in three acute stroke patients (all male, mean age 46.7±16.9, Berg Balance Scale 20±12.8). A logistic regression classifier was used to distinguish walking from baseline in these spectral EEG patterns. Mean classification accuracies of 94.0±5.4% and 93.1±7.9%, respectively, were reached when active and passive walking were compared against baseline. The classification performance between passive and active walking was 83.4±7.4%. A classification accuracy of 89.9±5.7% was achieved in the stroke patients when comparing walking and baseline. Furthermore, in the healthy volunteers modulation of low gamma activity in central midline areas was found to be associated with the gait cycle phases, but not in the stroke patients. Our results demonstrate the feasibility of BCI-based robotic-assisted training devices for gait rehabilitation.

This article investigates a possible Brain Computer Interface (BCI) based on semantic relations. The BCI determines which prime word a subject has in mind by presenting probe words using an intelligent algorithm. Subjects indicate when a presented probe word is related to the prime word by a single finger tap. The detection of the neural signal associated with this movement is used by the BCI to decode the prime word. The movement detector combined both the evoked (ERP) and induced (ERD) responses elicited with the movement. Single trial movement detection had an average accuracy of 67%. The decoding of the prime word had an average accuracy of 38% when using 100 probes and 150 possible targets, and 41% after applying a dynamic stopping criterium, reducing the average number of probes to 47. The article shows that the intelligent algorithm used to present the probe words has a significantly higher performance than a random selection of probes. Simulations demonstrate that the BCI also works with larger vocabulary sizes, and the performance scales logarithmically with vocabulary size.

Brain-computer interfaces (BCIs) are systems that use real-time analysis of neuroimaging data to determine the mental state of their user for purposes such as providing neurofeedback. Here, we investigate the feasibility of a BCI based on speech perception. Multivariate pattern classification methods were applied to single-trial EEG data collected during speech perception by native and non-native speakers. Two principal questions were asked: 1) Can differences in the perceived categories of pairs of phonemes be decoded at the single-trial level? 2) Can these same categorical differences be decoded across participants, within or between native-language groups? Results indicated that classification performance progressively increased with respect to the categorical status (within, boundary or across) of the stimulus contrast, and was also influenced by the native language of individual participants. Classifier performance showed strong relationships with traditional event-related potential measures and behavioral responses. The results of the cross-participant analysis indicated an overall increase in average classifier performance when trained on data from all participants (native and non-native). A second cross-participant classifier trained only on data from native speakers led to an overall improvement in performance for native speakers, but a reduction in performance for non-native speakers. We also found that the native language of a given participant could be decoded on the basis of EEG data with accuracy above 80%. These results indicate that electrophysiological responses underlying speech perception can be decoded at the single-trial level, and that decoding performance systematically reflects graded changes in the responses related to the phonological status of the stimuli. This approach could be used in extensions of the BCI paradigm to support perceptual learning during second language acquisition.

Semantic priming is usually studied by examining ERPs over many trials and subjects. This article aims at detecting semantic priming at the single-trial level. By using machine learning techniques it is possible to analyse and classify short traces of brain activity, which could, for example, be used to build a Brain Computer Interface (BCI). This article describes an experiment where subjects were presented with word pairs and asked to decide whether the words were related or not. A classifier was trained to determine whether the subjects judged words as related or unrelated based on one second of EEG data. The results show that the classifier accuracy when training per subject varies between 54% and 67%, and is significantly above chance level for all subjects (N = 12) and the accuracy when training over subjects varies between 51% and 63%, and is significantly above chance level for 11 subjects, pointing to a general effect.

Using a commercially produced listening training program with a sample of 69 first-year postsecondary vocational students, researchers found that the program did not achieve sustained improvement in listening comprehension, interpretation, evaluation, and application. It was concluded that short, intensive training may not be adequate for this skill area. (SK)