1,721,861 research outputs found
Different thallium-201 single-photon emission tomographic patterns in benign and aggressive meningiomas.
No full textDifferent thallium-201 single-photon emission tomographic patterns in benign and aggressive meningiomas.
Tedeschi E, Soricelli A, Brunetti A, Romano M, Bucciero A, Iaconetta G
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
Advanced computational approaches for EEG-Based decoding of neurodegenerative diseases
No full textL'obiettivo della tesi di dottorato è quello di illustrare i lavori di ricerca svolti per la progettazione e lo sviluppo di framework computazionali avanzati per l'analisi dei segnali elettroencefalografici (EEG), al fine di migliorare la diagnosi precoce delle malattie neurodegenerative (ND). La demenza è una delle principali cause di disabilità e mortalità a livello globale, e l'identificazione delle sue fasi iniziali rimane una sfida critica sia per scopi prognostici che terapeutici. La moderna concettualizzazione delle ND, in particolare della malattia di Alzheimer, configura il declino cognitivo come un continuum, lungo il quale popolazioni con una compensazione funzionale ancora sufficiente potrebbero costituire target ideali per i trial clinici precoci. In questo contesto, i segnali EEG possono fornire biomarcatori non invasivi e a basso costo, con il potenziale di catturare disfunzioni neurali associate alla neurodegenerazione. Tuttavia, la complessità intrinseca e la variabilità dell’EEG comportano sfide significative per un'interpretazione e un'analisi accurate.
La tesi affronta il modo in cui i modelli di Deep Learning (DL), in particolare i Transformers, e le tecniche di interpretabilità possano essere utilizzati per una classificazione robusta dei dati EEG, offrendo spunti sui cambiamenti cognitivi nelle fasi precliniche e prodromiche e superando la necessità di competenze specifiche per l'estrazione di caratteristiche consistenti e affidabili. Inoltre, vengono esplorati altri approcci che avanzano l'integrazione tra neuroscienze computazionali e Machine Learning (ML), includendo la modellazione biofisica della modulazione neurale in risposta a stimoli specifici.
In particolare, la prima parte del lavoro presenta un nuovo framework di Deep Learning basato sui segnali, progettato per distinguere tra il declino cognitivo soggettivo (SCD) e l’impairment cognitivo lieve (MCI) utilizzando EEG resting-state. I metodi mirano a catturare i segni prodromici della malattia di Alzheimer attraverso un modello di Transformer basato sul meccanismo di self-attention. Per migliorare l'affidabilità e la traslabilità clinica, il framework descritto è stato integrato con strumenti di interpretabilità. Nello specifico, il ruolo del meccanismo di self-attention all'interno dei modelli Transformer è stato esplorato sistematicamente per spiegare i processi decisionali, fornendo maggiore trasparenza sul focus dei modelli sui segnali in ingresso per differenziare SCD da MCI e dimostrando che queste informazioni potrebbero essere utilizzate per guidare l'identificazione di biomarcatori di compromissione cognitiva nei segnali EEG a riposo.
La seconda parte del lavoro di ricerca è focalizzata sull’implementazione di metodi computazionali per l'analisi delle risposte evocate, cioè dei potenziali evento-correlati (ERP) e della (de)sicronizzazione evento-correlata (ERD/ERS), nella neurodegenerazione. Vengono analizzati il meccanismo di risonanza motoria nelle fasi precoci della malattia di Parkinson, la modellazione causale dinamica per la classificazione degli ERP e gli effetti degli stimoli sensoriali sulle risposte elettrofisiologiche in uno scenario di interazione uomo-robot.The aim of this Ph.D. thesis is to illustrate the research works conducted to design and develop advanced computational frameworks for analyzing electroencephalographic (EEG) signals to improve the early diagnosis of neurodegenerative diseases (NDs). Dementia is one of the leading causes of disability and death worldwide, and the detection of its initial phases remains a critical challenge both for prognostic and therapeutic purposes. The modern conceptualization of NDs, and particularly of Alzheimer’s disease, assumes cognitive decline to develop as a continuum, along which populations with still sufficient functional compensation could be targeted for early clinical trials. In this context, EEG signals can provide non-invasive and cost-effective biomarkers, holding potential for capturing neural dysfunctions associated with neurodegeneration. Nonetheless, the inherent complexity and variability of EEG result in significant challenges for accurate interpretation and analysis.
This thesis addresses how Deep Learning (DL) models, particularly Transformers, and interpretability techniques can be leveraged for robust classification of EEG data, offering insights into subtle cognitive changes in preclinical and prodromal stages and overcoming the need for domain-specific expertise to extract consistent and reliable features. Furthermore, other approaches advancing the integration of computational neuroscience with Machine Learning (ML), including biophysical modeling of neural modulation in response to specific stimuli, are explored.
In particular, the first part of the work presents a novel signal-based Deep Learning framework for distinguishing between subjective cognitive decline (SCD) and mild cognitive impairment (MCI) using resting-state EEG. The methods aim to capture prodromal signs of Alzheimer’s disease through a state-of-the-art Transformer model based on the mechanism of self-attention. To enhance clinical trustworthiness and translatability, the previously described method is then integrated with interpretability tools. Specifically, the role of self-attention within Transformer models is systematically explored to explain decision-making processes, providing greater transparency into the models’ focus on the input signals for differentiating SCD from MCI and proving that this information could be used to guide the identification of biomarkers of cognitive impairment in resting-state EEG.
The second part of the research work presents computational methods for analyzing evoked responses, namely event-related potentials (ERP) and event-related (de)synchronization (ERD/ERS), in neurodegeneration, exploring motor resonance in early Parkinson’s disease, dynamic causal modeling for ERP classification, and the effects of sensory stimuli on electrophysiological responses in a Human-Robot Interaction scenario
Author response: Alterations of functional connectivity of the motor cortex in Fabry disease: An RS-fMRI study
No full textVariations 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
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
The metallurgy of copper in Italian Prehistory. New archaeometric data from Sardinia
No full textWhen looking at the earliest metallurgy in Italy, data from Sardinia indicates a beginning during the first half of the 4th millennium cal. BC, which was followed by very gradual development that intensified during the 3rd millennium. Here we present results of XRF analysis, combined with Monte Carlo simulations, undertaken on copper artefacts recovered from the renowned sanctuary of Monte d’Accoddi (northern Sardinia), chiefly belonging to a period between the 4th and the early 2nd millennium cal. BC. Some finds were of particular interest in that they contained a high or anomalous concentration of silve
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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