The gustatory areas of the brain include the primary (GI) and the secondary (GII) gustatory cerebral cortex. A spatial mechanism has been recently hypothesized to explain the taste quality recognition. This research investigates the spatial distribution of blood oxygen level dependent (BOLD) activations evoked in the human area GI by different tastants, aimed at building a chemotopic map. The chemotopic organization of the human GI was studied in seven healthy subjects by applying three taste stimuli (salty, sweet, neutral) to either side of the tongue, using a 5-min functional magnetic resonance imaging (fMRI) block-designed protocol, alternating periods of rest and stimulation. Data were analyzed by the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL). Unilateral tongue stimulation consistently evoked bilateral activation in area GI. Ipsilateral foci were generally larger and signal increases greater. The foci evoked by each tastant exhibited slightly but not significantly different mean coordinates, broad overlap and high interindividual variability; the salty stimuli generally evoked more anterior foci and sweet stimuli more posterior activation. Results confirm that the gustatory pathways from tongue to cortex are bilaterally distributed, with an ipsilateral predominance. Although distinct GI zones were activated by the different taste stimuli, a clear topographical organization could not be recognized. Possibly, fMRI technique is unable to resolve fine topographical arrangements or GI discriminative role for different tastants is subserved by another mechanism. Bilateral activation of the primary somatosensory area in the parietal cortex (contralateral predominance) and of middle insula (ipsilateral predominance) were also observed.

Physical activity is recommended in patients with type 1 diabetes (T1D), but therapy management still lacks efficient tools to avoid exercise-induced hypoglycemia. Machine learning represents a powerful solution in the field of decision support for diabetes management and its application to continuous glucose monitoring (CGM) data appears promising in pre-exercise prediction of upcoming adverse events. Aim of this study was to investigate the possibility to distinguish if a specific configuration of CGM metrics evaluated before starting of exercise is more prone to induce hypoglycemia after the start of the exercise session until the following day. A total of 47 CGM recordings from T1D children have been used to extract CGM metrics from pre-exercise CGM data. Acquisitions were labelled as HYPO or as NO-HYPO, respectively if belonging to subjects who experienced or did not experience hypoglycemia during the time following the exercise. Anthropometric characteristics and extracted features have been given as input to a decision tree classification algorithm to select those with the most predictive power. The selected features were then further evaluated with respect to the classification problem by using them as input to other three classification models: random forest, adaboost and gradient boosting. Performance results in terms of area under receiver operating characteristic (AUC) were as follows: 85.5%, 82.1%, 78.1% and 74.3% for decision tree, gradient boosting, random forest and adaboost, respectively. M-value, maximum glucose, time above 180 mg/dL and time above 250 mg/dL could have a role in predicting upcoming hypoglycemia prior the starting of exercise.

During physical exercise, the assessment of cardiac autonomic regulation through the analysis of heart-rate variability is difficult due to non-stationarity of RR intervals. The short duration of stationary epoch of RR-interval series makes not possible the computation of classical time-and frequency-domain parameters. Symbolic analysis can deal with short epoch of RR interval; thus, this study aims to apply symbolic analysis to analyze heart-rate variability of short-distance runners during training and competition. Data consists of RR-intervals extracted from 30s-long electrocardiograms acquired by KardiaMobile of Alivecor in 8 short-distance runners (1/7 M/F, 17[16;20] years) during training and competition. Symbolic analysis classifies a reduced number (in this study, three) of consecutive RR intervals in four patterns according to the sign and number of variations: no variation (0V); one variation (1V); two like variations (2LV); two unlike variations (2UV). An increase in low amount of variations (0V or 1V patterns) is usually linked with increased sympathetic control and vagal withdrawal, while an increase in high amount of variations (2LV and 2UV patterns) is usually linked with sympathetic withdrawal and increased vagal control. In our results, pattern 0V /2LV increases/decreases from rest to post exercise and decreases/increases during recovery, in both training and competition. Thus, our results confirm the opposite trends between low and high variations of symbolic patterns. In conclusion, symbolic analysis seems to be an efficient tool to characterize heart-rate variability during physical exercise at different level of psychophysical stress.

Alzheimer's Disease (AD) is the most common neurodegenerative disease. Its first stage, namely prodromal or Mild Cognitive Impairment (MCI), is characterized by slightly structural changes in the subcortical structures of the temporal lobe. Brain Magnetic Resonance (MR) is the most utilized neu-roimaging modality for the diagnosis of AD. Although an early therapeutic intervention during the initial stages of AD appears to have a positive impact on the progression of symptoms, its accurate diagnosis is still very difficult. Deep Learning (DL)-based decision-support systems hold great potential in generalizing even under subtle anatomical changes of the brain, like the ones caused by AD at its onset. To our knowledge, we were the first to develop a Convolutional Long Short-Term Memory (ConvLSTM)-based decision-support system and an improved version of it for the automatic diagnosis of AD from 3D brain MR. The research presented in this paper aims to extend their applicability to MCI for effectiveness verification through the development of CLAUDIA, a new on-cloud decision-support system for the automatic diagnosis of Alzheimer's prodromal stage and disease from 3D brain MR. To this aim, we selected 438 unenhanced scans from the ADNI-1 dataset, preprocessed them, and injected the preprocessed scans to the ConvLSTM-based neural network for automatic feature extraction and binary/multiclass classification. On test data, CLAUDIA achieved very encouraging results that highlight the superiority of the multiclass classifier in comparison to the two binary classifiers. On the basis of the achieved outcomes, we demonstrated that CLAUDIA, being the first to extend the applicability of a ConvLSTM-based neural network to MCI for effectiveness verification, represents a promising scan-, DL-based decision-support system for the automatic diagnosis of Alzheimer's prodromal stage and disease from 3D brain MR. Moreover, its cloud thus machine-independent nature ensures a full reproducibility of the implementation while guaranteeing cost saving and sustainability.

Heart rate (HR) recording is a non-invasive, cheap and time-efficient tool for continuous cardiovascular monitoring through wearable technologies in sport applications directly on field. Although, HR measures cannot inform equally on all aspects of cardiac responses to training, given the individual HR kinetic that depends on internal and external influencing factors. Knowledge of the training context is required to correctly compute and interpret HR-derived indices. Training context is characterized by the training phases, their distribution and training load. The aim of this study is to develop an algorithm for automatic detection of training phases in HR series to boost signal processing for athletic cardiovascular monitoring with wearable technologies. The algorithm computes the start and end times of the training phases. It exploits the variance of HR series computed over moving overlapping windows to detect automatically training transition phases. The algorithm was tested on HR series acquired during middle distance running and jogging. The algorithm showed promising results: mean errors were globally lower than 5 s and percentage error did not exceed 5%. Thus, the fully automatic algorithm for detection of training phases can boost HR signal processing for reliable computation and interpretation of HR-derived indices during continuous cardiovascular monitoring with wearable sensors in athletes.

The beneficial effects of physical activity for the prevention and management of several chronic diseases are widely recognized.Mathematical modeling of the effects of physical exercise in body metabolism and in particular its influence on the control of glucose homeostasis is of primary importance in the development of eHealth monitoring devices for a personalized medicine.Nonetheless, to date only a few mathematical models have been aiming at this specific purpose.We have developed a whole-body computational model of the effects on metabolic homeostasis of a bout of physical exercise.Built upon an existing model, it allows to detail better both subjects' characteristics and physical exercise, thus determining to a greater extent the dynamics of the hormones and the metabolites considered. Author summaryExercise has a great impact on human metabolism and the lack of physical activity represents one of the main causes of the metabolic disorders.The effectiveness of regular physical activity in the prevention and management of several chronic diseases is widely recognized.In the study of the metabolism and related disorders, mathematical models have proven useful in describing and quantifying physiological processes often not easily measurable in vivo.Formulating a model describing the metabolic responses to a physical exercise session is a challenging task since the effects vary depending on its intensity, duration, modality and are also dependent on the subjects' physical characteristics (e.g.age, gender, body weight, fitness status).To date, none of the existing computational models is able to provide this level of "personalization".Thus, starting from an existing model of fuel homeostasis during exercise, we have formulated a novel computational system that is more detailed in describing both the physical exercise and the subjects' characteristics.