Development and characterisation of a neurogenic model of brain cancer

Primary glioblastoma (GB), the most common and aggressive adult brain tumour, is refractory to conventional therapies and characterised by poor prognosis. GB displays striking cellular heterogeneity, with a sub-population, called Glioblastoma Stem Cells (GSCs), intrinsically resistant to therapy, hence the high rate of recurrence. Alterations of the tumour suppressor gene PTEN are prevalent in primary GBM, resulting in the inhibition of the polarity protein Lgl1 due to aPKC hyperactivation. Dysregulation of this molecular axis is one of the mechanisms involved in GSC maintenance. After demonstrating that the PTEN/aPKC/Lgl axis is conserved in Drosophila, I deregulated it in different cells populations of the nervous system in order to individuate the cells at the root of neurogenic brain cancers. This analysis identified the type II neuroblasts (NBs) as the most sensitive to alterations of this molecular axis. Type II NBs are a sub-population of Drosophila stem cells displaying a lineage similar to that of the mammalian neural stem cells. Following aPKC activation in these stem cells, I obtained an adult brain cancer model in Drosophila that summarises many phenotypic traits of human brain tumours. Fly tumours are indeed characterised by accumulation of highly proliferative immature cells and keep growing in the adult leading the affected animals to premature death. With the aim to understand the role of cell polarity disruption in this tumorigenic process I carried out a molecular characterisation and transcriptome analysis of brain cancers from our fly model. In summary, the model I built and partially characterised in this thesis work may help deepen our knowledge on human brain cancers by investigating many different aspects of this complicate disease.

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.

Human congenital cytomegalovirus infection: characteristics and pathogenesis of fetal brain damage

Human cytomegalovirus (HCMV) causes congenital neurological lifelong disabilities. The study analyzed 10 HCMV-infected human fetuses at 21 weeks of gestation to evaluate the characteristics and pathogenesis of brain injury related to congenital human CMV (cCMV) infection. Specifically, tissues from cortical and white matter areas, subventricular zone, thalamus, hypothalamus, hippocampus, basal ganglia and cerebellum were analysed by: i) immunohistochemistry (IHC) to detect HCMV-infected cell distribution, ii) hematoxylin-eosin staining to evaluate histological damage and iii) real-time PCR to quantify tissue viral load (HCMV-DNA). Viral tropism was assessed by double IHC to detect HCMV-antigens and neural/neuronal markers: nestin (expressed in early differentiation stage), doublecortin (DCX, identifying neuronal precursor cells) and neuronal nuclei (NeuN, identifying mature neurons). HCMV-positive cells and viral DNA were found in the brain of 8/10 (80%) fetuses. For these cases, brain damage was classified in mild (n=4, 50%), moderate (n=3, 37.5%) and severe (n=1, 12.5%) based on presence of i) diffuse astrocytosis, microglial activation and vascular changes; ii) occasional (in mild) or multiple (in moderate/severe) microglial nodules and iii) necrosis (in severe). The highest median HCMV-DNA level was found in the hippocampus (212 copies/5ng of humanDNA [hDNA], range: 10-7,505) as well as the highest mean HCMV-infected cell value (2.9 cells, range: 0-23), followed by that detected in subventricular zone (1.8 cells, range: 0-19). This suggests a preferential HCMV tropism for immature neuronal cells, residing in these regions, confirmed by the detection of DCX and nestin in 94% and 63.3% of HCMV-positive cells, respectively. NeuN was not found among HCMV-positive cells and was nearly absent in the brain with severe damage, suggesting HCMV does not infect mature neurons and immature HCMV-infected neuronal cells do not differentiate into neurons. HCMV preferential tropism in immature neural/neuronal cells delays/inhibits their differentiation interfering with brain development processes that lead to structural and functional brain defects.

Synthesis and characterization of novel selective NKCC1 inhibitors for the treatment of down syndrome and brain disorders characterized by depolarizing GABAergic transmission.

Proper GABAergic transmission through Cl-permeable GABAA receptors is fundamental for physiological brain development and function. Indeed, defective GABAergic signaling – due to a high NKCC1/KCC2 expression ratio – has been implicated in several neurodevelopmental disorders (e.g., Down syndrome, DS, Autism spectrum disorders, ASD). Interestingly, NKCC1 inhibition by the FDA-approved diuretic drug bumetanide reverts cognitive deficits in the TS65Dn mouse models of DS and core symptoms in other models of brain disorders. However, the required chronic treatment with bumetanide is burdened by its diuretic side effects caused by the antagonization of the kidney Cl importer NKCC2. This may lead to hypokalemia, while jeopardizing drug compliance. Crucially, these issues would be solved by selective NKCC1 inhibitors, thus devoid of the diuretic effect of bumetanide. To this aim, starting from bumetanide’s structure, we applied a ligand-based computational approach to design new molecular entities that we tested in vitro for their capacity to selectively block NKCC1. Extensive synthetic efforts and structure-activity relationships analyses allowed us to improve in vitro potency and overall drug-like properties of the initially identified chemical hits. As a result, we identified a new highly potent NKCC1 inhibitor (ARN23746) that displayed excellent solubility, metabolic stability, and no significant effect on NKCC2 in vitro. Moreover, this novel and selective NKCC1 inhibitor was able to rescue cognitive deficits in DS mice and social/repetitive behaviors in ASD mice, with no diuretic effect and no overt toxicity upon chronic treatment in adult animals. Thus, ARN23746 a selective NKCC1 inhibitor devoid of the diuretic effect – represents a suitable and solid therapeutic strategy for the treatment of Down syndrome and all the brain neurological disorders characterized by depolarizing GABAergic transmission.

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.

Bioimaging of animal embryogenesis: mathematical methods and computational algorithms

Some fundamental biological processes such as embryonic development have been preserved during evolution and are common to species belonging to different phylogenetic positions, but are nowadays largely unknown. The understanding of cell morphodynamics leading to the formation of organized spatial distribution of cells such as tissues and organs can be achieved through the reconstruction of cells shape and position during the development of a live animal embryo. We design in this work a chain of image processing methods to automatically segment and track cells nuclei and membranes during the development of a zebrafish embryo, which has been largely validates as model organism to understand vertebrate development, gene function and healingrepair mechanisms in vertebrates. The embryo is previously labeled through the ubiquitous expression of fluorescent proteins addressed to cells nuclei and membranes, and temporal sequences of volumetric images are acquired with laser scanning microscopy. Cells position is detected by processing nuclei images either through the generalized form of the Hough transform or identifying nuclei position with local maxima after a smoothing preprocessing step. Membranes and nuclei shapes are reconstructed by using PDEs based variational techniques such as the Subjective Surfaces and the Chan Vese method. Cells tracking is performed by combining informations previously detected on cells shape and position with biological regularization constraints. Our results are manually validated and reconstruct the formation of zebrafish brain at 7-8 somite stage with all the cells tracked starting from late sphere stage with less than 2% error for at least 6 hours. Our reconstruction opens the way to a systematic investigation of cellular behaviors, of clonal origin and clonal complexity of brain organs, as well as the contribution of cell proliferation modes and cell movements to the formation of local patterns and morphogenetic fields.

Development of Clinical Decision Support Systems based on Mathematical Models of Physiological Systems

In the last years of research, I focused my studies on different physiological problems. Together with my supervisors, I developed/improved different mathematical models in order to create valid tools useful for a better understanding of important clinical issues. The aim of all this work is to develop tools for learning and understanding cardiac and cerebrovascular physiology as well as pathology, generating research questions and developing clinical decision support systems useful for intensive care unit patients. I. ICP-model Designed for Medical Education We developed a comprehensive cerebral blood flow and intracranial pressure model to simulate and study the complex interactions in cerebrovascular dynamics caused by multiple simultaneous alterations, including normal and abnormal functional states of auto-regulation of the brain. Individual published equations (derived from prior animal and human studies) were implemented into a comprehensive simulation program. Included in the normal physiological modelling was: intracranial pressure, cerebral blood flow, blood pressure, and carbon dioxide (CO2) partial pressure. We also added external and pathological perturbations, such as head up position and intracranial haemorrhage. The model performed clinically realistically given inputs of published traumatized patients, and cases encountered by clinicians. The pulsatile nature of the output graphics was easy for clinicians to interpret. The manoeuvres simulated include changes of basic physiological inputs (e.g. blood pressure, central venous pressure, CO2 tension, head up position, and respiratory effects on vascular pressures) as well as pathological inputs (e.g. acute intracranial bleeding, and obstruction of cerebrospinal outflow). Based on the results, we believe the model would be useful to teach complex relationships of brain haemodynamics and study clinical research questions such as the optimal head-up position, the effects of intracranial haemorrhage on cerebral haemodynamics, as well as the best CO2 concentration to reach the optimal compromise between intracranial pressure and perfusion. We believe this model would be useful for both beginners and advanced learners. It could be used by practicing clinicians to model individual patients (entering the effects of needed clinical manipulations, and then running the model to test for optimal combinations of therapeutic manoeuvres). II. A Heterogeneous Cerebrovascular Mathematical Model Cerebrovascular pathologies are extremely complex, due to the multitude of factors acting simultaneously on cerebral haemodynamics. In this work, the mathematical model of cerebral haemodynamics and intracranial pressure dynamics, described in the point I, is extended to account for heterogeneity in cerebral blood flow. The model includes the Circle of Willis, six regional districts independently regulated by autoregulation and CO2 reactivity, distal cortical anastomoses, venous circulation, the cerebrospinal fluid circulation, and the intracranial pressure-volume relationship. Results agree with data in the literature and highlight the existence of a monotonic relationship between transient hyperemic response and the autoregulation gain. During unilateral internal carotid artery stenosis, local blood flow regulation is progressively lost in the ipsilateral territory with the presence of a steal phenomenon, while the anterior communicating artery plays the major role to redistribute the available blood flow. Conversely, distal collateral circulation plays a major role during unilateral occlusion of the middle cerebral artery. In conclusion, the model is able to reproduce several different pathological conditions characterized by heterogeneity in cerebrovascular haemodynamics and can not only explain generalized results in terms of physiological mechanisms involved, but also, by individualizing parameters, may represent a valuable tool to help with difficult clinical decisions. III. Effect of Cushing Response on Systemic Arterial Pressure. During cerebral hypoxic conditions, the sympathetic system causes an increase in arterial pressure (Cushing response), creating a link between the cerebral and the systemic circulation. This work investigates the complex relationships among cerebrovascular dynamics, intracranial pressure, Cushing response, and short-term systemic regulation, during plateau waves, by means of an original mathematical model. The model incorporates the pulsating heart, the pulmonary circulation and the systemic circulation, with an accurate description of the cerebral circulation and the intracranial pressure dynamics (same model as in the first paragraph). Various regulatory mechanisms are included: cerebral autoregulation, local blood flow control by oxygen (O2) and/or CO2 changes, sympathetic and vagal regulation of cardiovascular parameters by several reflex mechanisms (chemoreceptors, lung-stretch receptors, baroreceptors). The Cushing response has been described assuming a dramatic increase in sympathetic activity to vessels during a fall in brain O2 delivery. With this assumption, the model is able to simulate the cardiovascular effects experimentally observed when intracranial pressure is artificially elevated and maintained at constant level (arterial pressure increase and bradicardia). According to the model, these effects arise from the interaction between the Cushing response and the baroreflex response (secondary to arterial pressure increase). Then, patients with severe head injury have been simulated by reducing intracranial compliance and cerebrospinal fluid reabsorption. With these changes, oscillations with plateau waves developed. In these conditions, model results indicate that the Cushing response may have both positive effects, reducing the duration of the plateau phase via an increase in cerebral perfusion pressure, and negative effects, increasing the intracranial pressure plateau level, with a risk of greater compression of the cerebral vessels. This model may be of value to assist clinicians in finding the balance between clinical benefits of the Cushing response and its shortcomings. IV. Comprehensive Cardiopulmonary Simulation Model for the Analysis of Hypercapnic Respiratory Failure We developed a new comprehensive cardiopulmonary model that takes into account the mutual interactions between the cardiovascular and the respiratory systems along with their short-term regulatory mechanisms. The model includes the heart, systemic and pulmonary circulations, lung mechanics, gas exchange and transport equations, and cardio-ventilatory control. Results show good agreement with published patient data in case of normoxic and hyperoxic hypercapnia simulations. In particular, simulations predict a moderate increase in mean systemic arterial pressure and heart rate, with almost no change in cardiac output, paralleled by a relevant increase in minute ventilation, tidal volume and respiratory rate. The model can represent a valid tool for clinical practice and medical research, providing an alternative way to experience-based clinical decisions. In conclusion, models are not only capable of summarizing current knowledge, but also identifying missing knowledge. In the former case they can serve as training aids for teaching the operation of complex systems, especially if the model can be used to demonstrate the outcome of experiments. In the latter case they generate experiments to be performed to gather the missing data.

Sonic Hedgehog pathway impairment in Neural Precursor Cells of the Ts65Dn mouse, an animal model of Down syndrome

Mental retardation in Down syndrome (DS) has been imputed to the decreased brain volume, which is evident starting from the early phases of development. Recent studies in a widely used mouse model of DS, the Ts65Dn mouse, have shown that neurogenesis is severely impaired during the early phases of brain development, suggesting that this defect may be a major determinant of brain hypotrophy and mental retardation in individuals with DS. Recently, it has been found that in the cerebellum of Ts65Dn mice there is a defective responsiveness to Sonic Hedgehog (Shh), a potent mitogen that controls cell division during brain development, suggesting that failure of Shh signaling may underlie the reduced proliferation potency in DS. Based on these premises, we sought to identify the molecular mechanisms underlying derangement of the Shh pathway in neural precursor cells (NPCs) from Ts65Dn mice. We found that the expression levels of the Shh receptor Patched1 (Ptch1) were increased compared to controls both at the RNA and protein level. Partial silencing of Ptch1 expression in trisomic NPCs restored cell proliferation, indicating that proliferation impairment was due to Ptch1 overexpression. We further found that the overexpression of Ptch1 in trisomic NPCs is related to increased levels of AICD, a transcription-promoting fragment of amyloid precursor protein (APP). Increased AICD binding to the Ptch1 promoter favored its acetylated status, thus enhancing Ptch1 expression. Taken together, these data provide novel evidence that Ptch1 over expression underlies derangement of the Shh pathway in trisomic NPCs, with consequent proliferation impairment. The demonstration that Ptch1 over expression in trisomic NPCs is due to an APP fragment provides a link between this trisomic gene and the defective neuronal production that characterizes the DS brain.

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.

Preclinical development of anti-cancer strategies against human HER-2

HER-2 is a 185 kDa transmembrane receptor tyrosine kinase that belongs to the EGFR family. HER-2 is overexpressed in nearly 25% of human breast cancers and women with this subtype of breast cancer have a worse prognosis and frequently develop metastases. The progressive high number of HER-2-positive breast cancer patients with metastatic spread in the brain (up to half of women) has been attributed to the reduction in mortality, the effectiveness of Trastuzumab in killing metastatic cells in other organs and to its incapability to cross the blood-brain barrier. Apart from full-length-HER-2, a splice variant of HER-2 lacking exon 16 (here referred to as D16) was identified in human HER-2-positive breast cancers. Here, the contribution of HER-2 and D16 to mammary carcinogenesis was investigated in a model transgenic for both genes (F1 model). A dominant role of D16, especially in early stages of tumorigenesis, was suggested and the coexistence of heterogeneous levels of HER-2 and D16 in F1 tumors revealed the undeniable value of F1 strain as preclinical model of HER-2-positive breast cancer, closer resembling the human situation in respect to previous models. The therapeutical efficacy of anti-HER-2 agents, targeting HER-2 receptor (Trastuzumab, Lapatinib, R-LM249) or signaling effectors (Dasatinib, UO126, NVP-BKM120), was investigated in models of local or advanced HER-2-positive breast cancer. In contrast with early studies, data herein collected suggested that the presence of D16 can predict a better response to Trastuzumab and other agents targeting HER-2 receptor or Src activity. Using a multiorgan HER-2-positive metastatic model, the efficacy of NVP-BKM120 (PI3K inhibitor) in blocking the growth of brain metastases and the oncolytic ability of R-LM249 (HER-2-retargeted HSV) to reach and destroy metastatic HER-2-positive cancer cells were shown. Finally, exploiting the definition of “oncoantigen” given to HER-2, the immunopreventive activity of two vaccines on HER-2-positive mammary tumorigenesis was demonstrated.

Fractal descriptors for quantifying brain complexity in MRI

Magnetic Resonance Imaging (MRI) is the in vivo technique most commonly employed to characterize changes in brain structures. The conventional MRI-derived morphological indices are able to capture only partial aspects of brain structural complexity. Fractal geometry and its most popular index, the fractal dimension (FD), can characterize self-similar structures including grey matter (GM) and white matter (WM). Previous literature shows the need for a definition of the so-called fractal scaling window, within which each structure manifests self-similarity. This justifies the existence of fractal properties and confirms Mandelbrot’s assertion that "fractals are not a panacea; they are not everywhere". In this work, we propose a new approach to automatically determine the fractal scaling window, computing two new fractal descriptors, i.e., the minimal and maximal fractal scales (mfs and Mfs). Our method was implemented in a software package, validated on phantoms and applied on large datasets of structural MR images. We demonstrated that the FD is a useful marker of morphological complexity changes that occurred during brain development and aging and, using ultra-high magnetic field (7T) examinations, we showed that the cerebral GM has fractal properties also below the spatial scale of 1 mm. We applied our methodology in two neurological diseases. We observed the reduction of the brain structural complexity in SCA2 patients and, using a machine learning approach, proved that the cerebral WM FD is a consistent feature in predicting cognitive decline in patients with small vessel disease and mild cognitive impairment. Finally, we showed that the FD of the WM skeletons derived from diffusion MRI provides complementary information to those obtained from the FD of the WM general structure in T1-weighted images. In conclusion, the fractal descriptors of structural brain complexity are candidate biomarkers to detect subtle morphological changes during development, aging and in neurological diseases.

Development of functional MRI protocols in the study of neurodegenerative diseases: MRI study in patients with REM sleep behavior disorder, idiopathic Parkinson's disease and multisystem atrophy

In the central nervous system, iron in several proteins is involved in many important processes: oxygen transportation, oxidative phosphorylation, mitochondrial respiration, myelin production, the synthesis and metabolism of neurotransmitters. Abnormal iron homoeostasis can induce cellular damage through hydroxyl radical production, which can cause the oxidation, modification of lipids, proteins, carbohydrates, and DNA, lead to neurotoxicity. Moreover increased levels of iron are harmful and iron accumulations are typical hallmarks of brain ageing and several neurodegenerative disorders particularly PD. Numerous studies on post mortem tissue report on an increased amount of total iron in the substantia nigra in patients with PD also supported by large body of in vivo findings from Magnetic Resonance Imaging (MRI) studies. The importance and approaches for in vivo brain iron assessment using multiparametric MRI is increased over last years. Quantitative MRI may provide useful biomarkers for brain integrity assessment in iron-related neurodegeneration. Particularly, a prominent change in iron- sensitive T2* MRI contrast within the sub areas of the SN overlapping with nigrosome 1 were shown to be a hallmark of Parkinson's Disease with high diagnostic accuracy. Moreover, differential diagnosis between Parkinson's Disease (PD) and atypical parkinsonian syndromes (APS) remains challenging, mainly in the early phases of the disease. Advanced brain MR imaging enables to detect the pathological changes of nigral and extranigral structures at the onset of clinical manifestations and during the course of the disease. The Nigrosome-1 (N1) is a substructure of the healthy Substantia Nigra pars compacta enriched by dopaminergic neurons; their loss in Parkinson’s disease and atypical parkinsonian syndromes is related to the iron accumulation. N1 changes are supportive MR biomarkers for diagnosis of these neurodegenerative disorders, but its detection is hard with conventional sequences, also using high field (3T) scanner. Quantitative susceptibility mapping (QSM), an iron-sensitive technique, enables the direct detection of Neurodegeneration

Magnetic Resonance Imaging-Derived biomarkers of Isocitrate-Dehydrogenase mutation in diffuse gliomas: a conventional MR and Diffusion Weighted Imaging study.

The introduction of molecular criteria into the classification of diffuse gliomas has added interesting practical implications to glioma management. This has created a new clinical need for correlating imaging characteristics with glioma genotypes, also known as radiogenomics or imaging genomics. Whilst many studies have primarily focused on the use of advanced magnetic resonance imaging (MRI) techniques for radiogenomics purposes, conventional MRI sequences still remain the reference point in the study and characterization of brain tumours. Moreover, a different approach may rely on diffusion-weighted imaging (DWI) usage, which is considered a “conventional” sequence in line with recently published directions on glioma imaging. In a non-invasive way, it can provide direct insight into the microscopic physical properties of tissues. Considering that Isocitrate-Dehydrogenase gene mutations may reflect alterations in metabolism, cellularity, and angiogenesis, which may manifest characteristic features on an MRI, the identification of specific MRI biomarkers could be of great interest in managing patients with brain gliomas. My study aimed to evaluate the presence of specific MRI-derived biomarkers of IDH molecular status through conventional MRI and DWI sequences.

Impiego dell’ecografia ad alta risoluzione nella diagnosi di anomalie cerebrali fetali difficili. Focus sul complesso anteriore.

In questo lavoro di tesi vengono prese in esame le principali anomalie cerebrali fetali a carico del complesso anteriore, formato dal cavo del setto pellucido e dai corni frontali dei ventricoli laterali. Si è poi concentrata l’attenzione sull’oloprosencefalia e sull’obliterazione del cavo del setto pellucido, analizzando i casi che sono stati riferiti c/o la U.O. di Ostetricia e Medicina dell’Età Prenatale del Policlinico di S. Orsola – IRCCS. L’oloprosencefalia racchiude in sé uno spettro di anomalie cerebrali caratterizzate da un difetto di formazione della linea mediana con forme variabili di fusione degli emisferi cerebrali. Le forme alobari mostrano una distorsione della anatomia cerebrale, con un singolo ventricolo e sono spesso associate ad anomalie extracerebrali e del cariotipo. Nelle forme semilobari e lobari il setto pellucido è generalmente assente nei piani assiali, con corni frontali fusi ed ipoplasici, ma queste caratteristiche possono essere di difficile interpretazione ad un esame di screening. Le anomalie facciali sono invece più sfuggenti. L’obliterazione del cavo del setto consiste in un suo aspetto ecogeno, normalmente disteso da fluido; è ritenuta una variante della norma, ma queste conclusioni sono basate su casistiche limitate. Anche in questo caso abbiamo riportato l’eventuale presenza di anomalie associate ed abbiamo poi rivalutato questi bambini mediante una visita specialistica presso la U.O. di Neuropsichiatria Infantile. Nella nostra esperienza di 16 casi, la neurosonografia è stata in grado di definire la presenza o meno di anomalie cerebrali associate (1 caso di cisti interemisferiche e corpo calloso disgenetico) al pari della risonanza magnetica. Nei casi apparentemente isolati, in circa il 20% tale reperto è stato transitorio nel corso della gravidanza e non sono state riportate anomalie del cariotipo. Tutte le visite di follow up eseguite nel contesto dello studio (risultati parziali di 7/15 bambini) hanno dimostrato uno sviluppo nella norma per l’età del bambino.

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.