Nuove metodologie di analisi per studi di attività cerebrale nell'uomo mediante Risonanza Magnetica funzionale

Il perdono è l’atto emotivo e cognitivo che avviene quando una persona, che è stata offesa e ferita da un’altra persona, prova un sentimento di risentimento o impulsi di ritorsione verso di essa, ma decide di scusarla. Sebbene colui che perdona non assolve dalla sua colpa la persona che si è comportata male nei suoi confronti, egli compie una decisione cosciente di rinunciare alla sua rabbia ed ai sentimenti di vendetta. Questa tesi si propone di verificare l’ipotesi che il perdono eserciti un effetto positivo sul nostro organismo, perché perdonare rappresenta un modo per l’individuo di superare situazioni o eventi che altrimenti potrebbero rappresentare una causa di stress, sia dal punto di vista psicologico che neurobiologico, anche per un periodo di tempo prolungato, che può drammaticamente alterare l’equilibrio omeostatico biologico e mentale dell’individuo stesso, con conseguenze potenzialmente pericolose. Lo scopo di questo lavoro è di determinare le risposte neurobiologiche ed i correlati cerebrali associati all’evocazione immaginaria (ipotetica ma realistica) del comportamento legato al perdono ed al non perdono nell’uomo. Ci si propone in questo modo di fornire le basi per la comprensione delle basi biologiche e dell’importanza psicologica del perdono, un fenomeno che finora è stato studiato principalmente solo dal punto di vista filosofico e psicologico. Il paradigma sperimentale del protocollo di studio del progetto prevede l’uso della metodologia fMRI (functional magnetic resonance imaging), una sofisticata tecnica di imaging funzionale del cervello che permette di investigare le funzioni cerebrali in vivo in maniera non invasiva nell’uomo. Rispetto alla PET l’fMRI è meno invasiva (non si fa uso di composti radioattivi) e più versatile (per esempio si ha la possibilità di ripetere molti studi nella stessa sessione). Usando l’fMRI associata a paradigmi sperimentali opportunamente disegnati, siamo in grado di misurare la risposta del cervello a differenti tipi di stimolazioni: sensoriali, cognitive o emozionali. Dopo una panoramica sulle principali metodologie di esplorazione funzionale del cervello, si passa ad una descrizione dettagliata del paradigma sperimentale utilizzato nello studio e dei vari tipi di analisi dei dati fMRI che sono state effettuate. Segue una parte in cui si presentano i vari risultati ottenuti ed un loro discussione. In appendice si riportano i principi fisici dell’fMRI ed una descrizione delle basi neurobiologiche delle emozioni

Unsupervised clustering of MDS data using federated learning

In this master thesis we developed a model for unsupervised clustering on a data set of biomedical data. This data has been collected by GenoMed4All consortium from patients affected by Myelodysplastic Syndrome (MDS), that is an haematological disease. The main focus is put on the genetic mutations collected that are used as features of the patients in order to cluster them. Clustering approaches have been used in several studies concerning haematological diseases such MDS. A neural network-based model was used to solve the task. The results of the clustering have been compared with labels from a "gold standard'' technique, i.e. hierarchical Dirichlet processes (HDP). Our model was designed to be also implemented in the context of federated learning (FL). This innovative technique is able to achieve machine learning objective without the necessity of collecting all the data in one single center, allowing strict privacy policies to be respected. Federated learning was used because of its properties, and because of the sensitivity of data. Several recent studies regarding clinical problems addressed with machine learning endorse the development of federated learning settings in such context, because its privacy preserving properties could represent a cornerstone for applying machine learning techniques to medical data. In this work will be then discussed the clustering performance of the model, and also its generative capabilities

BGP and inter-AS economic relationships

The structure of the Internet is still unknown even if it pro- vides well-known services for a large part of the worldwide population. Its current conguration is the result of complex economic interaction developed in the last 20 years among important carriers and ISPs (i.e. ASes). Although with slight success, in the last few years some research work tried to shed light on the economic relationships established among ASes. Typical approaches employed in the above work proceed along two lines: rst, data from BGP monitors spread out all over the world is gath- ered to infer an Internet AS-level topology graph, and second heuristics taking as input this graph are applied to get economic tags associated to all edges between nodes (i.e. ASes). In this paper we propose an in- novative tagging approach leveraging on the lifetime of an AS path to infer the economic relationships on all edges joining the ASes crossed by the path itself, without cutting-o backup links, that bring economic information as well as stable links. The major ndings of our approach can be summarized as follows: (data hygiene before infer the Internet AS-level topology graph) study on AS paths loops, human error and their impact on data correctness ( life-time based tagging we do not cut-o bakcup links) we evidence those tags are inferred only from a partial viewpoint we evidence the maximum lifetime of the AS path that have contributed to infer the tag of each connection { classication of candidate Tier-1 AS based on three indexes re ecting the importance of an AS { explanation and life-time study of non valley-free AS path

Conditional granger causality analysis of fMRI data shows a direct connection from LGN to hMT+ bypassing V1

The human middle temporal complex (hMT+) is devoted to motion perception. To determine whether motion-related neural information may reach hMT+ directly from the thalamus, by-passing the primary visual cortex (V1), we measured effective connectivity in an optic flow fMRI experiment in humans. Conditional Granger Causality analysis was employed to measure direct influences between the lateral geniculate nucleus (LGN) and hMT+, discarding indirect effects mediated by V1. Results indicated the existence of a bilateral alternative pathway for visual motion processing that allows for a direct flow of information from LGN to hMT+. This direct link may play a role in blindsight

Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data

Recently, a novel microwave apparatus for breast lesion detection (MammoWave), uniquely able to function in air with 2 antennas rotating in the azimuth plane and operating within the band 1-9 GHz has been developed. Machine learning (ML) has been implemented to understand information from the frequency spectrum collected through MammoWave in response to the stimulus, segregating breasts with and without lesions. The study comprises 61 breasts (from 35 patients), each one with the correspondent output of the radiologist's conclusion (i.e., gold standard) obtained from echography and/or mammography and/or MRI, plus pathology or 1-year clinical follow-up when required. The MammoWave examinations are performed, recording the frequency spectrum, where the magnitudes show substantial discrepancy and reveals dissimilar behaviours when reflected from tissues with/without lesions. Principal component analysis is implemented to extract the unique quantitative response from the frequency response for automated breast lesion identification, engaging the support vector machine (SVM) with a radial basis function kernel. In-vivo feasibility validation (now ended) of MammoWave was approved in 2015 by the Ethical Committee of Umbria, Italy (N. 6845/15/AV/DM of 14 October 2015, N. 10352/17/NCAV of 16 March 2017, N 13203/18/NCAV of 17 April 2018). Here, we used a set of 35 patients. According to the radiologists conclusions, 25 breasts without lesions and 36 breasts with lesions underwent a MammoWave examination. The proposed SVM model achieved the accuracy, sensitivity, and specificity of 91%, 84.40%, and 97.20%. The proposed ML augmented MammoWave can identify breast lesions with high accuracy

Titanium Internal Fixator Removal in Maxillofacial Surgery: Is It Necessary? A Systematic Review and Meta-Analysis

: Titanium plates and screws are essential devices in maxillofacial surgery since late 1980s, but despite their wide use there is no consensus in titanium internal fixators removal after bone healing. A systematic literature review and meta-analysis were conducted on seventeen retrospective studies. Effect size and 95% confidence intervals were calculated for plate removal (per plate and per patient) and for removal causes (infection, pain, screws complications, exposition, palpability). Odds ratio, 95% confidence intervals, and χ 2 test were measured for sex, smoking, and implant site. Heterogeneity was evaluated with Cochran and Inconstancy test. Obtained data were used to design Forest and Funnel plots. The aim of the study is to identify and clarify reasons and risk factors for plates and screws removal. Infection is the most frequent reason; the habit of tobacco usage and implant site (mandibula) are the main risk factors. The administration of antibiotic prophylaxis is essential, and patients must quit smoking before and after surgery. In conclusion there is no scientific evidence supporting the removal of internal devices as mandatory step of the postoperative procedure

FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients

Federated learning (FL) is a distributed learning paradigm that facilitates collaborative training of a shared global model across devices while keeping data localized. The deployment of FL in numerous real-world applications faces delays, primarily due to the prevalent reliance on supervised tasks. Generating detailed labels at edge devices, if feasible, is demanding, given resource constraints and the imperative for continuous data updates. In addressing these challenges, solutions such as federated semi-supervised learning (FSSL), which relies on unlabeled clients' data and a limited amount of labeled data on the server, become pivotal. In this paper, we propose FedAnchor, an innovative FSSL method that introduces a unique double-head structure, called anchor head, paired with the classification head trained exclusively on labeled anchor data on the server. The anchor head is empowered with a newly designed label contrastive loss based on the cosine similarity metric. Our approach mitigates the confirmation bias and overfitting issues associated with pseudo-labeling techniques based on high-confidence model prediction samples. Extensive experiments on CIFAR10/100 and SVHN datasets demonstrate that our method outperforms the state-of-the-art method by a significant margin in terms of convergence rate and model accuracy

The Effect of Visual Experience on the Development of Functional Architecture in hMT+

We investigated whether the visual hMT+ cortex plays a role in supramodal representation of sensory flow, not mediated by visual mental imagery. We used functional magnetic resonance imaging to measure neural activity in sighted and congenitally blind individuals during passive perception of optic and tactile flows. Visual motion–responsive cortex, including hMT+, was identified in the lateral occipital and inferior temporal cortices of the sighted subjects by response to optic flow. Tactile flow perception in sighted subjects activated the more anterior part of these cortical regions but deactivated the more posterior part. By contrast, perception of tactile flow in blind subjects activated the full extent, including the more posterior part. These results demonstrate that activation of hMT+ and surrounding cortex by tactile flow is not mediated by visual mental imagery and that the functional organization of hMT+ can develop to subserve tactile flow perception in the absence of any visual experience. Moreover, visual experience leads to a segregation of the motion-responsive occipitotemporal cortex into an anterior subregion involved in the representation of both optic and tactile flows and a posterior subregion that processes optic flow only

How skill expertise shapes the brain functional architecture: an fMRI study of visuo-spatial and motor processing in professional racing-car and naïve drivers

The present study was designed to investigate the brain functional architecture that subserves visuo-spatial and motor processing in highly skilled individuals. By using functional magnetic resonance imaging (fMRI), we measured brain activity while eleven Formula racing-car drivers and eleven ‘naïve’ volunteers performed a motor reaction and a visuo-spatial task. Tasks were set at a relatively low level of difficulty such to ensure a similar performance in the two groups and thus avoid any potential confounding effects on brain activity due to discrepancies in task execution. The brain functional organization was analyzed in terms of regional brain response, inter-regional interactions and blood oxygen level dependent (BOLD) signal variability. While performance levels were equal in the two groups, as compared to naïve drivers, professional drivers showed a smaller volume recruitment of task-related regions, stronger connections among task-related areas, and an increased information integration as reflected by a higher signal temporal variability. In conclusion, our results demonstrate that, as compared to naïve subjects, the brain functional architecture sustaining visuo-motor processing in professional racing-car drivers, trained to perform at the highest levels under extremely demanding conditions, undergoes both ‘quantitative’ and ‘qualitative’ modifications that are evident even when the brain is engaged in relatively simple, non-demanding tasks. These results provide novel evidence in favor of an increased ‘neural efficiency’ in the brain of highly skilled individuals

It’s not all in your car: functional and structural correlates of exceptional driving skills in professional racers

Driving is a complex behavior that requires the integration of multiple cognitive functions. While many studies have investigated brain activity related to driving simulation under distinct conditions, little is known about the brain morphological and functional architecture in professional competitive driving, which requires exceptional motor and navigational skills. Here, 11 professional racing-car drivers and 11 “naïve” volunteers underwent both structural and functional brain magnetic resonance imaging (MRI) scans. Subjects were presented with short movies depicting a Formula One car racing in four different official circuits. Brain activity was assessed in terms of regional response, using an Inter-Subject Correlation (ISC) approach, and regional interactions by mean of functional connectivity. In addition, voxel-based morphometry (VBM) was used to identify specific structural differences between the two groups and potential interactions with functional differences detected by the ISC analysis. Relative to non-experienced drivers, professional drivers showed a more consistent recruitment of motor control and spatial navigation devoted areas, including premotor/motor cortex, striatum, anterior, and posterior cingulate cortex and retrosplenial cortex, precuneus, middle temporal cortex, and parahippocampus. Moreover, some of these brain regions, including the retrosplenial cortex, also had an increased gray matter density in professional car drivers. Furthermore, the retrosplenial cortex, which has been previously associated with the storage of observer-independent spatial maps, revealed a specific correlation with the individual driver's success in official competitions. These findings indicate that the brain functional and structural organization in highly trained racing-car drivers differs from that of subjects with an ordinary driving experience, suggesting that specific anatomo-functional changes may subtend the attainment of exceptional driving performance