Parallel modeling of the electric field distribution in the brain

The term "Brain Imaging" identi�es a set of techniques to analyze the structure and/or functional behavior of the brain in normal and/or pathological situations. These techniques are largely used in the study of brain activity. In addition to clinical usage, analysis of brain activity is gaining popularity in others recent �fields, i.e. Brain Computer Interfaces (BCI) and the study of cognitive processes. In this context, usage of classical solutions (e.g. f MRI, PET-CT) could be unfeasible, due to their low temporal resolution, high cost and limited portability. For these reasons alternative low cost techniques are object of research, typically based on simple recording hardware and on intensive data elaboration process. Typical examples are ElectroEncephaloGraphy (EEG) and Electrical Impedance Tomography (EIT), where electric potential at the patient's scalp is recorded by high impedance electrodes. In EEG potentials are directly generated from neuronal activity, while in EIT by the injection of small currents at the scalp. To retrieve meaningful insights on brain activity from measurements, EIT and EEG relies on detailed knowledge of the underlying electrical properties of the body. This is obtained from numerical models of the electric �field distribution therein. The inhomogeneous and anisotropic electric properties of human tissues make accurate modeling and simulation very challenging, leading to a tradeo�ff between physical accuracy and technical feasibility, which currently severely limits the capabilities of these techniques. Moreover elaboration of data recorded requires usage of regularization techniques computationally intensive, which influences the application with heavy temporal constraints (such as BCI). This work focuses on the parallel implementation of a work-flow for EEG and EIT data processing. The resulting software is accelerated using multi-core GPUs, in order to provide solution in reasonable times and address requirements of real-time BCI systems, without over-simplifying the complexity and accuracy of the head models.

Mental states monitoring through passive Brain-Computer Interface systems

The monitoring of cognitive functions aims at gaining information about the current cognitive state of the user by decoding brain signals. In recent years, this approach allowed to acquire valuable information about the cognitive aspects regarding the interaction of humans with external world. From this consideration, researchers started to consider passive application of brain–computer interface (BCI) in order to provide a novel input modality for technical systems solely based on brain activity. The objective of this thesis is to demonstrate how the passive Brain Computer Interfaces (BCIs) applications can be used to assess the mental states of the users, in order to improve the human machine interaction. Two main studies has been proposed. The first one allows to investigate whatever the Event Related Potentials (ERPs) morphological variations can be used to predict the users’ mental states (e.g. attentional resources, mental workload) during different reactive BCI tasks (e.g. P300-based BCIs), and if these information can predict the subjects’ performance in performing the tasks. In the second study, a passive BCI system able to online estimate the mental workload of the user by relying on the combination of the EEG and the ECG biosignals has been proposed. The latter study has been performed by simulating an operative scenario, in which the occurrence of errors or lack of performance could have significant consequences. The results showed that the proposed system is able to estimate online the mental workload of the subjects discriminating three different difficulty level of the tasks ensuring a high reliability.

Innovative Man Machine Interfaces In Aeronautics

The research activity focused on the study, design and evaluation of innovative human-machine interfaces based on virtual three-dimensional environments. It is based on the brain electrical activities recorded in real time through the electrical impulses emitted by the brain waves of the user. The achieved target is to identify and sort in real time the different brain states and adapt the interface and/or stimuli to the corresponding emotional state of the user. The setup of an experimental facility based on an innovative experimental methodology for “man in the loop" simulation was established. It allowed involving during pilot training in virtually simulated flights, both pilot and flight examiner, in order to compare the subjective evaluations of this latter to the objective measurements of the brain activity of the pilot. This was done recording all the relevant information versus a time-line. Different combinations of emotional intensities obtained, led to an evaluation of the current situational awareness of the user. These results have a great implication in the current training methodology of the pilots, and its use could be extended as a tool that can improve the evaluation of a pilot/crew performance in interacting with the aircraft when performing tasks and procedures, especially in critical situations. This research also resulted in the design of an interface that adapts the control of the machine to the situation awareness of the user. The new concept worked on, aimed at improving the efficiency between a user and the interface, and gaining capacity by reducing the user’s workload and hence improving the system overall safety. This innovative research combining emotions measured through electroencephalography resulted in a human-machine interface that would have three aeronautical related applications: • An evaluation tool during the pilot training; • An input for cockpit environment; • An adaptation tool of the cockpit automation.

1° level of automation: the effectiveness of adaptive cruise control on driving and visual behaviour

The research activities have allowed the analysis of the driver assistance systems, called Advanced Driver Assistance Systems (ADAS) in relation to road safety. The study is structured according to several evaluation steps, related to definite on-site tests that have been carried out with different samples of users, according to their driving experience with the ACC. The evaluation steps concern: •The testing mode and the choice of suitable instrumentation to detect the driver’s behaviour in relation to the ACC. •The analysis modes and outputs to be obtained, i.e.: - Distribution of attention and inattention; - Mental workload; - The Perception-Reaction Time (PRT), the Time To Collision (TTC) and the Time Headway (TH). The main purpose is to assess the interaction between vehicle drivers and ADAS, highlighting the inattention and variation of the workloads they induce regarding the driving task. The research project considered the use of a system for monitoring visual behavior (ASL Mobile Eye-XG - ME), a powerful GPS that allowed to record the kinematic data of the vehicle (Racelogic Video V-BOX) and a tool for reading brain activity (Electroencephalographic System - EEG). Just during the analytical phase, a second and important research objective was born: the creation of a graphical interface that would allow exceeding the frame count limit, making faster and more effective the labeling of the driver’s points of view. The results show a complete and exhaustive picture of the vehicle-driver interaction. It has been possible to highlight the main sources of criticalities related to the user and the vehicle, in order to concretely reduce the accident rate. In addition, the use of mathematical-computational methodologies for the analysis of experimental data has allowed the optimization and verification of analytical processes with neural networks that have made an effective comparison between the manual and automatic methodology.

Models for the study of neuronal activity during cognitive tasks

Assessment of brain connectivity among different brain areas during cognitive or motor tasks is a crucial problem in neuroscience today. Aim of this research study is to use neural mass models to assess the effect of various connectivity patterns in cortical EEG power spectral density (PSD), and investigate the possibility to derive connectivity circuits from EEG data. To this end, two different models have been built. In the first model an individual region of interest (ROI) has been built as the parallel arrangement of three populations, each one exhibiting a unimodal spectrum, at low, medium or high frequency. Connectivity among ROIs includes three parameters, which specify the strength of connection in the different frequency bands. Subsequent studies demonstrated that a single population can exhibit many different simultaneous rhythms, provided that some of these come from external sources (for instance, from remote regions). For this reason in the second model an individual ROI is simulated only with a single population. Both models have been validated by comparing the simulated power spectral density with that computed in some cortical regions during cognitive and motor tasks. Another research study is focused on multisensory integration of tactile and visual stimuli in the representation of the near space around the body (peripersonal space). This work describes an original neural network to simulate representation of the peripersonal space around the hands, in basal conditions and after training with a tool used to reach the far space. The model is composed of three areas for each hand, two unimodal areas (visual and tactile) connected to a third bimodal area (visual-tactile), which is activated only when a stimulus falls within the peripersonal space. Results show that the peripersonal space, which includes just a small visual space around the hand in normal conditions, becomes elongated in the direction of the tool after training, thanks to a reinforcement of synapses.

Role of BDNF secretion and signaling in the activity-dependent refinement of synaptic connections

Neuronal networks exhibit diverse types of plasticity, including the activity-dependent regulation of synaptic functions and refinement of synaptic connections. In addition, continuous generation of new neurons in the “adult” brain (adult neurogenesis) represents a powerful form of structural plasticity establishing new connections and possibly implementing pre-existing neuronal circuits (Kempermann et al, 2000; Ming and Song, 2005). Neurotrophins, a family of neuronal growth factors, are crucially involved in the modulation of activity-dependent neuronal plasticity. The first evidence for the physiological importance of this role evolved from the observations that the local administration of neurotrophins has dramatic effects on the activity-dependent refinement of synaptic connections in the visual cortex (McAllister et al, 1999; Berardi et al, 2000; Thoenen, 1995). Moreover, the local availability of critical amounts of neurotrophins appears to be relevant for the ability of hippocampal neurons to undergo long-term potentiation (LTP) of the synaptic transmission (Lu, 2004; Aicardi et al, 2004). To achieve a comprehensive understanding of the modulatory role of neurotrophins in integrated neuronal systems, informations on the mechanisms about local neurotrophins synthesis and secretion as well as ditribution of their cognate receptors are of crucial importance. In the first part of this doctoral thesis I have used electrophysiological approaches and real-time imaging tecniques to investigate additional features about the regulation of neurotrophins secretion, namely the capability of the neurotrophin brain-derived neurotrophic factor (BDNF) to undergo synaptic recycling. In cortical and hippocampal slices as well as in dissociated cell cultures, neuronal activity rapidly enhances the neuronal expression and secretion of BDNF which is subsequently taken up by neurons themselves but also by perineuronal astrocytes, through the selective activation of BDNF receptors. Moreover, internalized BDNF becomes part of the releasable source of the neurotrophin, which is promptly recruited for activity-dependent recycling. Thus, we described for the first time that neurons and astrocytes contain an endocytic compartment competent for BDNF recycling, suggesting a specialized form of bidirectional communication between neurons and glia. The mechanism of BDNF recycling is reminiscent of that for neurotransmitters and identifies BDNF as a new modulator implicated in neuro- and glio-transmission. In the second part of this doctoral thesis I addressed the role of BDNF signaling in adult hippocampal neurogenesis. I have generated a transgenic mouse model to specifically investigate the influence of BDNF signaling on the generation, differentiation, survival and connectivity of newborn neurons into the adult hippocampal network. I demonstrated that the survival of newborn neurons critically depends on the activation of the BDNF receptor TrkB. The TrkB-dependent decision regarding life or death in these newborn neurons takes place right at the transition point of their morphological and functional maturation Before newborn neurons start to die, they exhibit a drastic reduction in dendritic complexity and spine density compared to wild-type newborn neurons, indicating that this receptor is required for the connectivity of newborn neurons. Both the failure to become integrated and subsequent dying lead to impaired LTP. Finally, mice lacking a functional TrkB in the restricted population of newborn neurons show behavioral deficits, namely increased anxiety-like behavior. These data suggest that the integration and establishment of proper connections by newly generated neurons into the pre-existing network are relevant features for regulating the emotional state of the animal.

Neuroprotective activity of guanosine in an in vitro model of Alzheimer's disease

The β-Amyloid (βA) peptide is the major component of senile plaques that are one of the hallmarks of Alzheimer’s Disease (AD). It is well recognized that Aβ exists in multiple assembly states, such as soluble oligomers or insoluble fibrils, which affect neuronal viability and may contribute to disease progression. In particular, common βA-neurotoxic mechanisms are Ca2+ dyshomeostasis, reactive oxygen species (ROS) formation, altered signaling, mitochondrial dysfunction and neuronal death such as necrosis and apoptosis. Recent study shows that the ubiquitin-proteasome pathway play a crucial role in the degradation of short-lived and regulatory proteins that are important in a variety of basic and pathological cellular processes including apoptosis. Guanosine (Guo) is a purine nucleoside present extracellularly in brain that shows a spectrum of biological activities, both under physiological and pathological conditions. Recently it has become recognized that both neurons and glia also release guanine-based purines. However, the role of Guo in AD is still not well established. In this study, we investigated the machanism basis of neuroprotective effects of GUO against Aβ peptide-induced toxicity in neuronal (SH-SY5Y), in terms of mitochondrial dysfunction and translocation of phosphatidylserine (PS), a marker of apoptosis, using MTT and Annexin-V assay, respectively. In particular, treatment of SH-SY5Y cells with GUO (12,5-75 μM) in presence of monomeric βA25-35 (neurotoxic core of Aβ), oligomeric and fibrillar βA1-42 peptides showed a strong dose-dependent inhibitory effects on βA-induced toxic events. The maximum inhibition of mitochondrial function loss and PS translocation was observed with 75 μM of Guo. Subsequently, to investigate whether neuroprotection of Guo can be ascribed to its ability to modulate proteasome activity levels, we used lactacystin, a specific inhibitor of proteasome. We found that the antiapoptotic effects of Guo were completely abolished by lactacystin. To rule out the possibility that this effects resulted from an increase in proteasome activity by Guo, the chymotrypsin-like activity was assessed employing the fluorogenic substrate Z-LLL-AMC. The treatment of SH-SY5Y with Guo (75 μM for 0-6 h) induced a strong increase, in a time-dependent manner, of proteasome activity. In parallel, no increase of ubiquitinated protein levels was observed at similar experimental conditions adopted. We then evaluated an involvement of anti and pro-apoptotic proteins such as Bcl-2, Bad and Bax by western blot analysis. Interestingly, Bax levels decreased after 2 h treatment of SH-SY5Y with Guo. Taken together, these results demonstrate that Guo neuroprotective effects against βA-induced apoptosis are mediated, at least partly, via proteasome activation. In particular, these findings suggest a novel neuroprotective pathway mediated by Guo, which involves a rapid degradation of pro-apoptotic proteins by the proteasome. In conclusion, the present data, raise the possibility that Guo could be used as an agent for the treatment of AD.

Models for the Study of Cortical Activity During Cognitive ans Motor Tasks

This thesis is mainly devoted to show how EEG data and related phenomena can be reproduced and analyzed using mathematical models of neural masses (NMM). The aim is to describe some of these phenomena, to show in which ways the design of the models architecture is influenced by such phenomena, point out the difficulties of tuning the dozens of parameters of the models in order to reproduce the activity recorded with EEG systems during different kinds of experiments, and suggest some strategies to cope with these problems. In particular the chapters are organized as follows: chapter I gives a brief overview of the aims and issues addressed in the thesis; in chapter II the main characteristics of the cortical column, of the EEG signal and of the neural mass models will be presented, in order to show the relationships that hold between these entities; chapter III describes a study in which a NMM from the literature has been used to assess brain connectivity changes in tetraplegic patients; in chapter IV a modified version of the NMM is presented, which has been developed to overcomes some of the previous version’s intrinsic limitations; chapter V describes a study in which the new NMM has been used to reproduce the electrical activity evoked in the cortex by the transcranial magnetic stimulation (TMS); chapter VI presents some preliminary results obtained in the simulation of the neural rhythms associated with memory recall; finally, some general conclusions are drawn in chapter VII.

Methods for the estimation of the cortical activity and connectivity during cognitive tasks in humans

The research activity characterizing the present thesis was mainly centered on the design, development and validation of methodologies for the estimation of stationary and time-varying connectivity between different regions of the human brain during specific complex cognitive tasks. Such activity involved two main aspects: i) the development of a stable, consistent and reproducible procedure for functional connectivity estimation with a high impact on neuroscience field and ii) its application to real data from healthy volunteers eliciting specific cognitive processes (attention and memory). In particular the methodological issues addressed in the present thesis consisted in finding out an approach to be applied in neuroscience field able to: i) include all the cerebral sources in connectivity estimation process; ii) to accurately describe the temporal evolution of connectivity networks; iii) to assess the significance of connectivity patterns; iv) to consistently describe relevant properties of brain networks. The advancement provided in this thesis allowed finding out quantifiable descriptors of cognitive processes during a high resolution EEG experiment involving subjects performing complex cognitive tasks.

Brain-Computer Interfaces for augmented communication: Asynchronous and adaptive algorithms and evaluation with end users

This thesis aimed at addressing some of the issues that, at the state of the art, avoid the P300-based brain computer interface (BCI) systems to move from research laboratories to end users’ home. An innovative asynchronous classifier has been defined and validated. It relies on the introduction of a set of thresholds in the classifier, and such thresholds have been assessed considering the distributions of score values relating to target, non-target stimuli and epochs of voluntary no-control. With the asynchronous classifier, a P300-based BCI system can adapt its speed to the current state of the user and can automatically suspend the control when the user diverts his attention from the stimulation interface. Since EEG signals are non-stationary and show inherent variability, in order to make long-term use of BCI possible, it is important to track changes in ongoing EEG activity and to adapt BCI model parameters accordingly. To this aim, the asynchronous classifier has been subsequently improved by introducing a self-calibration algorithm for the continuous and unsupervised recalibration of the subjective control parameters. Finally an index for the online monitoring of the EEG quality has been defined and validated in order to detect potential problems and system failures. This thesis ends with the description of a translational work involving end users (people with amyotrophic lateral sclerosis-ALS). Focusing on the concepts of the user centered design approach, the phases relating to the design, the development and the validation of an innovative assistive device have been described. The proposed assistive technology (AT) has been specifically designed to meet the needs of people with ALS during the different phases of the disease (i.e. the degree of motor abilities impairment). Indeed, the AT can be accessed with several input devices either conventional (mouse, touchscreen) or alterative (switches, headtracker) up to a P300-based BCI.

Cognitive Assessment and Rehabilitation of subjects with Traumatic Brain Injury

This thesis regards the study and the development of new cognitive assessment and rehabilitation techniques of subjects with traumatic brain injury (TBI). In particular, this thesis i) provides an overview about the state of art of this new assessment and rehabilitation technologies, ii) suggests new methods for the assessment and rehabilitation and iii) contributes to the explanation of the neurophysiological mechanism that is involved in a rehabilitation treatment. Some chapters provide useful information to contextualize TBI and its outcome; they describe the methods used for its assessment/rehabilitation. The other chapters illustrate a series of experimental studies conducted in healthy subjects and TBI patients that suggest new approaches to assessment and rehabilitation. The new proposed approaches have in common the use of electroencefalografy (EEG). EEG was used in all the experimental studies with a different purpose, such as diagnostic tool, signal to command a BCI-system, outcome measure to evaluate the effects of a treatment, etc. The main achieved results are about: i) the study and the development of a system for the communication with patients with disorders of consciousness. It was possible to identify a paradigm of reliable activation during two imagery task using EEG signal or EEG and NIRS signal; ii) the study of the effects of a neuromodulation technique (tDCS) on EEG pattern. This topic is of great importance and interest. The emerged founding showed that the tDCS can manipulate the cortical network activity and through the research of optimal stimulation parameters, it is possible move the working point of a neural network and bring it in a condition of maximum learning. In this way could be possible improved the performance of a BCI system or to improve the efficacy of a rehabilitation treatment, like neurofeedback.

Functional and neural mechanisms of human fear conditioning: studies in healthy and brain-damaged individuals

Fear conditioning represents the learning process by which a stimulus, after repeated pairing with an aversive event, comes to evoke fear and becomes intrinsically aversive. This learning is essential to organisms throughout the animal kingdom and represents one the most successful laboratory paradigm to reveal the psychological processes that govern the expression of emotional memory and explore its neurobiological underpinnings. Although a large amount of research has been conducted on the behavioural or neural correlates of fear conditioning, some key questions remain unanswered. Accordingly, this thesis aims to respond to some unsolved theoretic and methodological issues, thus furthering our understanding of the neurofunctional basis of human fear conditioning both in healthy and brain-damaged individuals. Specifically, in this thesis, behavioural, psychophysiological, lesion and non-invasive brain stimulation studies were reported. Study 1 examined the influence of normal aging on context-dependent recall of extinction of fear conditioned stimulus. Study 2 aimed to determine the causal role of the ventromedial PFC (vmPFC) in the acquisition of fear conditioning by systematically test the effect of bilateral vmPFC brain-lesion. Study 3 aimed to interfere with the reconsolidation process of fear memory by the means of non-invasive brain stimulation (i.e. TMS) disrupting PFC neural activity. Finally, Study 4 aimed to investigate whether the parasympathetic – vagal – modulation of heart rate might reflect the anticipation of fearful, as compared to neutral, events during classical fear conditioning paradigm. Evidence reported in this PhD thesis might therefore provide key insights and deeper understanding of critical issues concerning the neurofunctional mechanisms underlying the acquisition, the extinction and the reconsolidation of fear memories in humans.

Altered transcriptional and epigenetic regulation affects brain cells proliferation and differentiation in the ultra-rare genetic demyelinating disease AGC1 deficiency: a study on in vitro models

AGC1 deficiency is a rare demyelinating disease caused by mutations in the SLC25A12 gene, which encodes for the mitochondrial glutamate-aspartate carrier 1 (AGC1/Alarar), highly expressed in the central nervous system. In neurons, impairment in AGC1 activity leads to reduction in N-acetyl-aspartate, the main lipid precursor for myelin synthesis (Profilo et al., 2017); in oligodendrocytes progenitors cells, AGC1 down regulation has been related to early arrest proliferation and premature differentiation (Petralla et al., 2019). Additionally, in vivo AGC1 deficiency models i.e., heterozygous mice for AGC1 knock-out and neurospheres from their subventricular zone, respectively, showed a global decrease in cells proliferation and a switch in neural stem cells (NSCs) commitment, with specific reduction in OPCs number and increase in neural and astrocytic pools (Petralla et al., 2019). Therefore, the present study aims to investigate the transcriptional and epigenetic regulation underlying the alterations observed in OPCs and NSCs biological mechanisms, in either AGC1 deficiency models of Oli-neu cells (murine immortalized oligodendrocytes precursors cells), partially silenced by a shRNA for SLC25A12 gene, and SVZ-derived neurospheres from AGC1+/- mice. Western blot and immunofluorescence analysis revealed significant variations in the expression of transcription factors involved in brain cells’ proliferation and differentiation, in association with altered histone post-translational modifications, as well as histone acetylases (HATs) and deacetylases (HDACs) activity/expression, suggesting an improper transcriptional and epigenetic regulation affecting both AGC1 deficiency in vitro models. Furthermore, given the large role of acetylation in controlling in specific time-windows OPC maturation (Hernandez and Casaccia; 2015), pharmacological HATs/HDACs inhibitions were performed, confirming the involvement of chromatin remodelling enzymes in the altered proliferation and early differentiation observed in the AGC1 deficiency models of siAGC1 Oli-neu cells and AGC1+/- mice-derived neurospheres.

Assessing brain connectivity through electroencephalographic signal processing and modeling analysis

Brain functioning relies on the interaction of several neural populations connected through complex connectivity networks, enabling the transmission and integration of information. Recent advances in neuroimaging techniques, such as electroencephalography (EEG), have deepened our understanding of the reciprocal roles played by brain regions during cognitive processes. The underlying idea of this PhD research is that EEG-related functional connectivity (FC) changes in the brain may incorporate important neuromarkers of behavior and cognition, as well as brain disorders, even at subclinical levels. However, a complete understanding of the reliability of the wide range of existing connectivity estimation techniques is still lacking. The first part of this work addresses this limitation by employing Neural Mass Models (NMMs), which simulate EEG activity and offer a unique tool to study interconnected networks of brain regions in controlled conditions. NMMs were employed to test FC estimators like Transfer Entropy and Granger Causality in linear and nonlinear conditions. Results revealed that connectivity estimates reflect information transmission between brain regions, a quantity that can be significantly different from the connectivity strength, and that Granger causality outperforms the other estimators. A second objective of this thesis was to assess brain connectivity and network changes on EEG data reconstructed at the cortical level. Functional brain connectivity has been estimated through Granger Causality, in both temporal and spectral domains, with the following goals: a) detect task-dependent functional connectivity network changes, focusing on internal-external attention competition and fear conditioning and reversal; b) identify resting-state network alterations in a subclinical population with high autistic traits. Connectivity-based neuromarkers, compared to the canonical EEG analysis, can provide deeper insights into brain mechanisms and may drive future diagnostic methods and therapeutic interventions. However, further methodological studies are required to fully understand the accuracy and information captured by FC estimates, especially concerning nonlinear phenomena.

Pathogenetic mechanisms of gastrointestinal symptoms in a mouse model of Fabry disease: insights on the microbiota-gut-brain axis

Fabry disease (FD), X-linked metabolic disorder caused by a deficiency in α-galactosidase A activity, leads to the accumulation of glycosphingolipids, mainly Gb3 and lyso-Gb3, in several organs. Gastrointestinal (GI) symptoms are among the earliest and most common, strongly impacting patients’ quality of life. However, the origin of these symptoms and the exact mechanisms of pathogenesis are still poorly understood, thus the pressing need to improve their knowledge. Here we aimed to evaluate whether a FD murine model (α-galactosidase A Knock-Out) captures the functional GI issues experienced by patients. In particular, the potential mechanisms involved in the development and maintenance of GI symptoms were explored by looking at the microbiota-gut-brain axis involvement. Moreover, we sought to examine the effects of lyso-Gb3 on colonic contractility and the intestinal epithelium and the enteric nervous system, which together play important roles in regulating intestinal ion transport and fluid and electrolyte homeostasis. Fabry mice revealed visceral hypersensitivity and a diarrhea-like phenotype accompanied by anxious-like behavior and reduced locomotor activity. They reported also an imbalance of SCFAs and an early compositional and functional dysbiosis of the gut microbiota, which partly persisted with advancing age. Moreover, overexpression of TRPV1 was found in affected mice, and partial alteration of TRPV4 and TRPA1 as well, identifying them as possible therapeutic targets. The Ussing chamber results after treatment with lyso-Gb3 showed an increase in Isc (likely mediated by HCO3- ions movement) which affects neuron-mediated secretion, especially capsaicin- and partly veratridine-mediated. This first characterization of gut-brain axis dysfunction in FD mouse provides functional validation of the model, suggesting new targets and possible therapeutic approaches. Furthermore, lyso-Gb3 is confirmed to be not only a marker for the diagnosis and follow-up of FD but also a possible player in the alteration of the FD colonic ion transport process.