Upper gut heat shock proteins HSP70 and GRP78 promote insulin resistance, hyperglycemia, and non-alcoholic steatohepatitis.

A high-fat diet increases the risk of insulin resistance, type-2 diabetes, and non-alcoholic steato-hepatitis. Here we identified two heat-shock proteins, Heat-Shock-Protein70 and Glucose-Regulated Protein78, which are increased in the jejunum of rats on a high-fat diet. We demonstrated a causal link between these proteins and hepatic and whole-body insulin-resistance, as well as the metabolic response to bariatric/metabolic surgery.

Metabolic surgery improves insulin resistance through the reduction of gut-secreted heat shock proteins.

Metabolic surgery improves insulin resistance and is associated with the remission of type 2 diabetes, but the mechanisms involved remain unknown. We find that human jejunal mucosa secretes heat shock proteins (HSPs) in vitro, in particular HSP70 and GRP78. Circulating levels of HSP70 are higher in people resistant to insulin, compared to the healthy and normalize after duodenal-jejunal bypass.

Downregulation of Insulin Sensitivity After Oral Glucose Administration: Evidence for the Anti-Incretin Effect.

Intestinal nutrients stimulate insulin secretion more potently than intravenous (IV) glucose administration under similar plasma glucose levels (incretin effect). According to the anti-incretin theory, intestinal nutrients should also cause a reduction of insulin sensitivity and/or secretion (anti-incretin effect) to defend against hyperinsulinemia-hypoglycemia. An exaggerated anti-incretin effect could contribute to insulin resistance/type 2 diabetes, whereas reduction of anti-incretin signals might explain diabetes improvement after bariatric surgery.

Glycemic control after metabolic surgery: a Granger causality and graph analysis.

The purpose of this study was to examine the contribution of nonesterified fatty acids (NEFA) and incretin to insulin resistance and diabetes amelioration after malabsorptive metabolic surgery that induces steatorrhea. In fact, NEFA infusion reduces glucose-stimulated insulin secretion, and high-fat diets predict diabetes development. Six healthy controls, 11 obese subjects, and 10 type 2 diabetic (T2D) subjects were studied before and 1 mo after biliopancreatic diversion (BPD).

Insulin Signaling in Insulin Resistance States and Cancer: A Modeling Analysis.

Insulin resistance is the common denominator of several diseases including type 2 diabetes and cancer, and investigating the mechanisms responsible for insulin signaling impairment is of primary importance. A mathematical model of the insulin signaling network (ISN) is proposed and used to investigate the dose-response curves of components of this network. Experimental data of C2C12 myoblasts with phosphatase and tensin homologue (PTEN) suppressed and data of L6 myotubes with induced insulin resistance have been analyzed by the model.

Avionic technology testing by using a cognitive neurometric index: A study with professional helicopter pilots.

In this study, we investigated the possibility to evaluate the impact of different avionic technologies on the mental workload of helicopter's pilots by measuring their brain activity with the EEG during a series of simulated missions carried out at AgustaWestland facilities in Yeovil (UK). The tested avionic technologies were: i) Head-Up Display (HUD); ii) Head-Mounted Display (HMD); iii) Full Conformal symbology (FC); iv) Flight Guidance (FG) symbology; v) Synthetic Vision System (SVS); and vi) Radar Obstacles (RO) detection system. It has been already demonstrated that in cognitive tasks, when the cerebral workload increases the EEG power spectral density (PSD) in theta band over frontal areas increases, and the EEG PSD in alpha band decreases over parietal areas.

Quantitative Assessment of the Training Improvement in a Motor-Cognitive Task by Using EEG, ECG and EOG Signals.

Generally, the training evaluation methods consist in experts supervision and qualitative check of the operator's skills improvement by asking them to perform specific tasks and by verifying the final performance. The aim of this work is to find out if it is possible to obtain quantitative information about the degree of the learning process throughout the training period by analyzing neuro-physiological signals, such as the electroencephalogram, the electrocardiogram and the electrooculogram. In fact, it is well known that such signals correlate with a variety of cognitive processes, e.

Investigating statistical differences in connectivity patterns properties at single subject level: a new resampling approach.

Methods based on the multivariate autoregressive (MVAR) approach are commonly used for effective connectivity estimation as they allow to include all available sources into a unique model. To ensure high levels of accuracy for high model dimensions, all the observations are used to provide a unique estimation of the model, and thus of the network and its properties. The unavailability of a distribution of connectivity values for a single experimental condition prevents to perform statistical comparisons between different conditions at a single subject level.

Individual cortical connectivity changes after stroke: a resampling approach to enable statistical assessment at single-subject level.

One of the main limitations commonly encountered when dealing with the estimation of brain connectivity is the difficulty to perform a statistical assessment of significant changes in brain networks at a single-subject level. This is mainly due to the lack of information about the distribution of the connectivity estimators at different conditions. While group analysis is commonly adopted to perform a statistical comparison between conditions, it may impose major limitations when dealing with the heterogeneity expressed by a given clinical condition in patients.

A new descriptor of neuroelectrical activity during BCI-assisted motor imagery-based training in stroke patients.

In BCI applications for stroke rehabilitation, BCI systems are used with the aim of providing patients with an instrument that is capable of monitoring and reinforcing EEG patterns generated by motor imagery (MI). In this study we proposed an offline analysis on data acquired from stroke patients subjected to a BCI-assisted MI training in order to define an index for the evaluation of MI-BCI training session which is independent from the settings adopted for the online control and which is able to describe the properties of neuroelectrical activations across sessions. Results suggest that such index can be adopted to sort the trails within a session according to the adherence to the task.

Occipital sources of resting-state alpha rhythms are related to local gray matter density in subjects with amnesic mild cognitive impairment and Alzheimer's disease.

Occipital sources of resting-state electroencephalographic (EEG) alpha rhythms are abnormal, at the group level, in patients with amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD). Here, we evaluated the hypothesis that amplitude of these occipital sources is related to neurodegeneration in occipital lobe as measured by magnetic resonance imaging. Resting-state eyes-closed EEG rhythms were recorded in 45 healthy elderly (Nold), 100 MCI, and 90 AD subjects.

Self-calibration algorithm in an asynchronous P300-based brain-computer interface.

Objective: Reliability is a desirable characteristic of brain-computer interface (BCI) systems when they are intended to be used under non-experimental operating conditions. In addition, their overall usability is influenced by the complex and frequent procedures that are required for configuration and calibration. Earlier studies examined the issue of asynchronous control in P300-based BCIs, introducing dynamic stopping and automatic control suspension features.

Investigating the effects of a sensorimotor rhythm-based BCI training on the cortical activity elicited by mental imagery.

Objective: It is well known that to acquire sensorimotor (SMR)-based brain-computer interface (BCI) control requires a training period before users can achieve their best possible performances. Nevertheless, the effect of this training procedure on the cortical activity related to the mental imagery ability still requires investigation to be fully elucidated. The aim of this study was to gain insights into the effects of SMR-based BCI training on the cortical spectral activity associated with the performance of different mental imagery tasks.

Influence of P300 latency jitter on event related potential-based brain-computer interface performance.

Objective: Several ERP-based brain-computer interfaces (BCIs) that can be controlled even without eye movements (covert attention) have been recently proposed. However, when compared to similar systems based on overt attention, they displayed significantly lower accuracy. In the current interpretation, this is ascribed to the absence of the contribution of short-latency visual evoked potentials (VEPs) in the tasks performed in the covert attention modality.

Aged-related changes in brain activity classification with respect to age by means of graph indexes.

Recent studies have investigated changes in the human brain network organization during the normal aging. A reduction of the connectivity between brain areas was demonstrated by combining neuroimaging technologies and graph theory. Clustering, characteristic path length and small-worldness are key topological measures and they are widely used in literature.

The effect of normalization of Partial Directed Coherence on the statistical assessment of connectivity patterns: a simulation study.

Partial Directed Coherence (PDC) is a spectral multivariate estimator for effective connectivity, relying on the concept of Granger causality. Even if its original definition derived directly from information theory, two modifies were introduced in order to provide better physiological interpretations of the estimated networks: i) normalization of the estimator according to rows, ii) squared transformation. In the present paper we investigated the effect of PDC normalization on the performances achieved by applying the statistical validation process on investigated connectivity patterns under different conditions of Signal to Noise ratio (SNR) and amount of data available for the analysis.

Advanced methods for time-varying effective connectivity estimation in memory processes.

Memory processes are based on large cortical networks characterized by non-stationary properties and time scales which represent a limitation to the traditional connectivity estimation methods. The recent development of connectivity approaches able to consistently describe the temporal evolution of large dimension connectivity networks, in a fully multivariate way, represents a tool that can be used to extract novel information about the processes at the basis of memory functions. In this paper, we applied such advanced approach in combination with the use of state-of-the-art graph theory indexes, computed on the connectivity networks estimated from high density electroencephalographic (EEG) data recorded in a group of healthy adults during the Sternberg Task.

A new statistical approach for the extraction of adjacency matrix from effective connectivity networks.

Graph theory is a powerful mathematical tool recently introduced in neuroscience field for quantitatively describing the main properties of investigated connectivity networks. Despite the technical advancements provided in the last few years, further investigations are needed for overcoming actual limitations in the field. In fact, the absence of a common procedure currently applied for the extraction of the adjacency matrix from a connectivity pattern has been leading to low consistency and reliability of ghaph indexes among the investigated population.

Asynchronous gaze-independent event-related potential-based brain-computer interface.

Objective: In this study a gaze independent event related potential (ERP)-based brain computer interface (BCI) for communication purpose was combined with an asynchronous classifier endowed with dynamical stopping feature. The aim was to evaluate if and how the performance of such asynchronous system could be negatively affected in terms of communication efficiency and robustness to false positives during the intentional no-control state.

Material And Methods: The proposed system was validated with the participation of 9 healthy subjects.

Nutrient infusion bypassing duodenum-jejunum improves insulin sensitivity in glucose-tolerant and diabetic obese subjects.

The mechanisms of type 2 diabetes remission after bariatric surgery is still not fully elucidated. In the present study, we tried to simulate the Roux-en-Y gastric bypass with a canonical or longer biliary limb by infusing a liquid formula diet into different intestinal sections. Nutrients (Nutrison Energy) were infused into mid- or proximal jejunum and duodenum during three successive days in 10 diabetic and 10 normal glucose-tolerant subjects.

Jejunal proteins secreted by db/db mice or insulin-resistant humans impair the insulin signaling and determine insulin resistance.

Background: Two recent studies demonstrated that bariatric surgery induced remission of type 2 diabetes very soon after surgery and far too early to be attributed to weight loss. In this study, we sought to explore the mechanism/s of this phenomenon by testing the effects of proteins from the duodenum-jejunum conditioned-medium (CM) of db/db or Swiss mice on glucose uptake in vivo in Swiss mice and in vitro in both Swiss mice soleus and L6 cells. We studied the effect of sera and CM proteins from insulin resistant (IR) and insulin-sensitive subjects on insulin signaling in human myoblasts.

Towards the time varying estimation of complex brain connectivity networks by means of a General Linear Kalman Filter approach.

One of the main limitations of the brain functional connectivity estimation methods based on Autoregressive Modeling, like the Granger Causality family of estimators, is the hypothesis that only stationary signals can be included in the estimation process. This hypothesis precludes the analysis of transients which often contain important information about the neural processes of interest. On the other hand, previous techniques developed for overcoming this limitation are affected by problems linked to the dimension of the multivariate autoregressive model (MVAR), which prevents from analysing complex networks like those at the basis of most cognitive functions in the brain.