1,720,981 research outputs found
Effect of EDA-driven sympathetic responses on the central processing of faces cued by hedonic odors: a preliminary ERP study
Full text linkOlfactory stimuli are powerful cues capable of modulating behavioral and physiological responses to other stimuli, such as visual stimuli. In this study, we investigated the integration of autonomic-driven information into the central nervous system dynamics during a contextual presentation of neutral faces and hedonic odors. To this aim, we simultaneously acquired the electrodermal activity (EDA) and EEG signals from a group of 20 healthy volunteers. We applied a novel methodological approach to identify event-related potentials (ERPs) with and without a concomitant EDA-related sympathetic response. Then, we investigated the effect of both sympathetic responses and contextual odors on ERP components involved in the processing of faces. Preliminary results showed a significant increase of the N170 amplitude in the left parieto-temporal region when a sympathetic response was present, compared to ERPs associated with no sympathetic responses, irrespective of odors' valence. This may suggest that sympathetic responses identified from EDA have an effect on the early central processing of faces with background odors, possibly reflecting enhanced arousal triggered by salient features of the stimuli
Il ruolo dell'accoppiamento fisiologico nelle dinamiche sociali: applicazione della causalità di Granger ai segnali di conduttanza cutanea
No full textQuesta tesi esplora il ruolo dell'accoppiamento elettrofisiologico nelle interazioni sociali attraverso l'applicazione dei metodi di Granger causality ai segnali EDA (Electrodermal Activity). Le variazioni della conduttanza cutanea, associate alla sudorazione in risposta a stimoli socio-emotivi, sono state registrate durante le interazioni tra i soggetti, consentendo di analizzare il grado di sincronia tra i loro segnali. In particolare, la Granger causality è stata utilizzata per stimare l'entità dell'accoppiamento fisiologico nei segnali EDA e per analizzare le relazioni causali tra le risposte emotive degli individui. Questo ha permesso di inferire la forza della loro connessione psicologica e, in generale, di comprendere meglio le dinamiche psico-emotive dell'interazione.
This thesis explores the role of electrophysiological coupling in social interactions through the application of Granger causality methods to EDA (Electrodermal Activity) signals. Variations in skin conductance, associated with sweating in response to socio-emotional stimuli, were recorded during interactions between subjects, allowing for an analysis of the degree of synchronization in their signals. Specifically, Granger causality was used to estimate the extent of physiological coupling in the EDA signals and to analyze the causal relationships between individuals' emotional responses. This allowed for inferences regarding the strength of the psychological connection between individuals and, more broadly, helped to capture the psycho-emotional dynamics of the interaction
Gli odori corporei modulano la percezione dei volti: studio delle dinamiche cerebrali attraverso l'analisi del segnale elettroencefalografico
No full textNel corso della tesi ho implementato un approccio metodologico rigoroso, robusto e replicabile per analizzare le dinamiche cerebrali durante un task di riconoscimento di espressioni facciali mostrate in combinazione con l'esposizione a odori corporei.
È noto che gli odori sono in grado di integrare le informazioni provenienti dai sensi considerati maggiori, come la vista, quando queste risultano ambigue. Questo sembra accadere anche quando le informazioni visive ambigue sono di tipo sociale, come ad esempio l'espressione facciale assunta da una persona. Infatti da studi comportamentali si evince che gli odori influenzano le modalità con cui le espressioni facciali vengono processate, ma i risultati sono contrastanti fra loro e rendono necessaria un'analisi dell'interazione fra olfatto e vista.
Per approfondire questo aspetto è stato predisposto un protocollo sperimentale che ha previsto l'acquisizione del segnale EEG durante un task di riconoscimento di espressioni facciali ambigue mostrate in combinazione con l'esposizione ad odori corporei. È stata scelta questa classe di odori in quanto gli odori corporei emessi da una persona sono in grado di fornire informazioni sociali rilevanti su di essa.
Il segnale così ottenuto è stato analizzato attraverso due approcci distinti: l'analisi dei potenziali evento-correlati, per lo studio all'interno di ogni singola regione cerebrale, e l'analisi di connettività, per lo studio delle modalità con cui queste comunicano
Clarifying CLARITY: quantitative optimization of the diffusion based delipidation protocol for genetically labelled tissue
Full text linkTissue clarification has been recently proposed to allow deep tissue imaging without light scattering. The clarification parameters are somewhat arbitrary and dependent on tissue type, source and dimension: every laboratory has its own protocol, but a quantitative approach to determine the optimum clearing time is still lacking. Since the use of transgenic mouse lines that express fluorescent proteins to visualize specific cell populations is widespread, a quantitative approach to determine the optimum clearing time for genetically labeled neurons from thick murine brain slices using CLARITY2 is described. In particular, as the main objective of the delipidation treatment is to clarify tissues, while limiting loss of fluorescent signal, the goodness of clarification was evaluated by considering the bulk tissue clarification index (BTCi) and the fraction of the fluorescent marker retained in the slice as easily quantifiable macroscale parameters. Here we describe the approach, illustrating an example of how it can be used to determine the optimum clearing time for 1 mm-thick cerebellar slice from transgenic L7GFP mice, in which Purkinje neurons express the GFP (green fluorescent protein) tag. To validate the method, we evaluated confocal stacks of our samples using standard image processing indices (i.e. the mean pixel intensity of neurons and the contrast-to-noise ratio) as figures of merit for image quality.The results show that detergent-based delipidation for more than five days does not increase tissue clarity but the fraction of GFP in the tissue continues to diminish. The optimum clearing time for 1 mm-thick slices was thus identified as five days, which is the best compromise between the increase in light penetration depth due to removal of lipids and a decrease in fluorescent signal as a consequence of protein loss: further clearing does not improve tissue transparency, but only leads to more protein removal or degradation. The rigorous quantitative approach described can be generalized to any clarification method to identify the moment when the clearing process should be terminated to avoid useless protein loss
3D reconstruction of single Purkinje Neurons from clarified cerebellar tissue for the study of sexual dimorphism in animal models of autism
No full textThis thesis aims to study sexual dimorphisms in animal models of autism through the investigation of morphological differences of Purkinje Cells (PCs) in the mouse cerebellum. Several studies available in the literature confirm that there is a close relationship between cell morphology and cell function. Thus, imaging neural structures in their native 3D arrangement may help our understanding of neural function. To this end, a new scheme of work is designed to obtain faithful information on the 3D structure of PCs in knock out and wild type mice of both sexes. In particular, the CLARITY2 protocol, which is a method for tissue clarification, is integrated with 3D imaging techniques and methods. The workflow adopted can be divided into two parts. First, in order to reach the best conditions of clarification (i.e. a compromise between signal goodness and signal loss), a rigorous optimization of the CLARITY2 protocol for 1 mm thick brain slices from L7GFP mice is proposed. The need of clarifying tissues derives from the presence of lipids in biological tissues. In fact, these cause significant light scattering and thus represent one of the major limiting factors for imaging purposes (i.e. confocal acquisitions). CLARITY2, which represents a simplified version of the CLARITY method, is a clearing tissue technique based on removing tissue lipids and replacing them with an optically transparent hydrogel. Therefore, clarified tissues are optically transparent but still preserve the 3D arrangement of structures within them. In this thesis, clarified tissue slices from L7GFP mice cerebella are studied at 3, 5 and 7 days of clarification. Slices immersed in PBS are used as controls. Three different analyses are performed on both clarified tissues and control at the same time points. First, macroscopic imaging is performed in order to quantify the bulk slice transparency. Then, single photon confocal microscopy imaging is done: Mean pixel intensity (MPI) and Contrast-to-Noise ratio (CNR) as markers of the amount of clarification are calculated at different depths in the sample. Moreover, the release of GFP from the specimens in the CLARITY solution is quantified in order to monitor the loss of signal from the samples.
A wide range of neural developmental abnormalities have been observed in Austim-Spectrum-Disorder (ASD). The cerebellum is one of the most affected regions in such disorder, both structurally and functionally. Among the different type of cells within the cerebellum, the PCs seem to play an important role in the development of ASD and thus represent an interesting feld of study. In some fields of research animal models constitute the only way for study: this is the case of disease modeling. Concerning ASD, different murine models have been presented to date. One of the most promising of such models is the Engrailed2 (En2) knock-out mouse, which harbors cerebellar abnormalities that are similar to those found in autistic individuals. For these reasons, finding the best day for the clearing treatment, tissue slices of L7GFP WT (wild-type) and L7GFP/En2-/- mutant mice cerebella are clarified. Because of the unbalanced incidence of ASD between male and female individuals (i.e. 4:1 ratio), for each genotype both sexed mice are considered and confocal image stacks of the treated tissues are acquired. In order to isolate single PCs from the datasets obtained, a smart region growing algorithm (SmRG algorithm) based on local features of the intensity value histogram is developed in Matlab environment (The Mathworks, Inc.). To allow an easy use of the algorithm, this is integrated in a Matlab Graphical-User- Interface (GUI). Single purkinje neurons from the four groups of mice are isolated. A morphologic analysis in terms of Surface-Area-to-Volume ratio as a raw index of neuron complexity is done
Brain connectivity of the central control of breathing in humans using EEG and fMRI: integration of data and hypothesis driven approaches
No full textOne of the grand challenges in neuroscience is the characterization of brain interactions regulating breathing, a complex process governed by central and peripheral nervous systems and primarily involved in several disorders as first sign of disease onset.
In the light of this challenge, this PhD thesis investigates brain networks involved in the physiological and pathological central control of breathing, exploiting respiratory signals, electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI). Hypothesis driven and data driven approaches were integrated in different methodological frameworks to perform exploratory analyses of brain connectivity from EEG or fMRI. In particular, each framework addresses specific challenges arising from clinical and biomedical research.
A first framework integrating i) Independent Component Analysis, ii) unsupervised clustering, iii) Multivariate Autoregressive modelling and iv) permutation-bootstrap statistics was developed for the group-level analysis of asymmetrical causal interactions from EEG recordings when the clinical setup involves low-density EEG caps, and when experimental protocols deal with resting-state/block design acquisitions. This framework was applied to two case studies. The first study involves patients with impaired breathing and affected by Cheyne-Stokes Respiration (CSR), and is focused on the assessment of the effects of typical CSR breathing pattern on brain connectivity. The second involves healthy subjects performing voluntary breath hold, and aims to investigate the effects at the brain level of the increase of carbon dioxide, as well as to establish potential differences in brain response to hypercapnia between the physiological and pathological case. The developed framework allowed to identify statistically significant differences in connectivity based on ventilatory conditions in both pathological and physiological case. Moreover, such differences occurred mostly in a frequency band associated with hypercapnic stimulation, holding for physiological plausibility of observed differences.
A second framework integrating i) Independent Component Analysis, ii) component classification, iii) temporal and spatial correlation analysis and iv) regression analysis was developed for the study of functional brain connectivity on fMRI data from healthy subjects under CO2 stimulation through gas administration. The framework addresses several issues related to the study of brain function related to breathing. On one hand, it provides a pipeline of analysis of fMRI data when the expected time course of brain activation is difficult to be specified a priori due to poor physiological characterisation of delay between gas administration and brain response, on the other it defines an approach for disentangling non-specific effects related to gas stimulation from brain response to CO2. This framework was applied to a population of healthy subjects under hypercapnic-normoxyc stimulation induced by two different tasks: a voluntary breath hold and a carbon dioxide gas administration task. The study is focused on the evaluation of the sensitivity of different groups of chemoreceptors to different values of carbon dioxide. The framework allowed to identify brain regions more sensitive to differences in CO2.
The methodological frameworks developed in this thesis raise from clinical and/or research challenges in brain imaging related to breathing. Nonetheless, the purpose of such frameworks is that of giving a methodology for the study of brain interactions in the presence of specific challenges that may arise also in other kind of studies. In this view, this thesis gives a pipeline of analysis of EEG data acquired with low-density caps and during resting-state/block-design that can be applied to other studies not strictly related to breathing. On the other hand, beyond the specific purpose of identifying a dose-dependent relationship between brain function and different levels of CO2, the fMRI pipeline presented in this thesis can be applied for the analysis of other breathing tasks inducing hypercapnia
SVILUPPO DI ALGORITMI DI INTELLIGENZA ARTIFICIALE PER LA DIAGNOSI DI MALATTIA CORONARICA DA IMMAGINI TAC
No full textLa tesi si concentra sullo sviluppo di algoritmi di intelligenza artificiale per la diagnosi della malattia coronarica (CAD) a partire da immagini acquisite tramite Angio-TC coronarica. Dopo una panoramica clinica sulla CAD e sulle tecniche di imaging, viene presentato un approccio basato su Deep Learning per la segmentazione automatica delle arterie coronarie. Il modello adottato è la rete nnU-Net v2, capace di adattarsi automaticamente al dataset utilizzato, in questo caso ImageCAS. La rete è stata addestrata e validata tramite una procedura di cross-validation e successivamente testata su un dataset esterno acquisito con tecnologia Photon Counting CT, presente in Fondazione Monasterio. L’obiettivo finale è migliorare la diagnosi automatica e oggettiva delle stenosi coronariche tramite la segmentazione del lume vascolare e la misura delle aree luminali lungo la centerline. Il metodo proposto ha mostrato risultati promettenti nella segmentazione automatica delle arterie coronariche, mentre il calcolo delle aree vascolari richiede ulteriori ottimizzazioni per raggiungere una maggiore accuratezza e affidabilità.
The thesis focuses on the development of artificial intelligence algorithms for the diagnosis of coronary artery disease (CAD) from images acquired through coronary CT angiography (CCTA). After a clinical overview of CAD and imaging techniques, a Deep Learning-based approach is presented for the automatic segmentation of coronary arteries. The adopted model is the nnU-Net v2 network, which can automatically adapt to the dataset used—in this case, ImageCAS. The network was trained and validated through a cross-validation procedure and subsequently tested on an external dataset acquired with Photon Counting CT technology, available at Fondazione Monasterio. The ultimate goal is to improve the automatic and objective diagnosis of coronary stenosis by segmenting the vascular lumen and measuring luminal areas along the centerline. The proposed method showed promising results in the automatic segmentation of coronary arteries, while the calculation of vascular areas still requires further optimization to achieve greater accuracy and reliability
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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