Functional connectivity analysis of cerebellum using spatially constrained spectral clustering

The human cerebellum contains almost 50% of the neurons in the brain, although its volume does not exceed 10% of the total brain volume. The goal of this study is to derive the functional network of the cerebellum during the resting-state and then compare the ensuing group networks between males and females. Toward this direction, a spatially constrained version of the classic spectral clustering algorithm is proposed and then compared against conventional spectral graph theory approaches, such as spectral clustering, and N-cut, on synthetic data as well as on resting-state fMRI data obtained from the Human Connectome Project (HCP). The extracted atlas was combined with the anatomical atlas of the cerebellum resulting in a functional atlas with 46 regions of interest. As a final step, a gender-based network analysis of the cerebellum was performed using the data-driven atlas along with the concept of the minimum spanning trees. The simulation analysis results confirm the dominance of the spatially constrained spectral clustering approach in discriminating activation patterns under noisy conditions. The network analysis results reveal statistically significant differences in the optimal tree organization between males and females. In addition, the dominance of the left VI lobule in both genders supports the results reported in a previous study of ours. To our knowledge, the extracted atlas comprises the first resting-state atlas of the cerebellum based on HCP data

FCLAB:An EEGLAB module for performing functional connectivity analysis on single-subject EEG data

Functional connectivity (FC) analysis constitutes a fundamental neuroscientific approach that has been extensively used for the investigation of brain's connectivity and activation patterns. To that end, several software tools have been developed. This paper presents FCLAB, the only EEGLAB-based plugin, which is able to work with EEG signals in order to estimate and visualize brain functional connectivity networks based on a variety of similarity measures as well as run a complete graph analysis procedure followed by a detailed visualization of the ensuing local and global measures distribution. FCLAB entails optimization procedures for the implementation of the connectivity structures and is the result of long-term research in EEG functional connectivity. The computed functional connectivity measures have been carefully selected to reflect the state-of-art in the field. Future work focuses on extending the platform for multi-subject analysis in order to enable the implementation of statistical analysis tools

A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy

Background: Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools. Method: Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death. Results: The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification: it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively. Conclusions: The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general

Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to Crystallized IQ and Gender

During the last years, it has been established that the prefrontal and posterior parietal brain lobes, which are mostly related to intelligence, have many connections to cerebellum. However, there is a limited research investigating cerebellum's relationship with cognitive processes. In this study, the network of cerebellum was analyzed in order to investigate its overall organization in individuals with low and high crystallized Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were selected from 136 subjects in resting-state from the Human Connectome Project (HCP) database and were further separated into two IQ groups composed of 69 low-IQ and 67 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas. Thereafter, correlation matrices were constructed by computing Pearson's correlation coefficients between the average BOLD time-series for each pair of ROIs inside the cerebellum. By computing conventional graph metrics, small-world network properties were verified using the weighted clustering coefficient and the characteristic path length for estimating the trade-off between segregation and integration. In addition, a connectivity metric was computed for extracting the average cost per network. The concept of the Minimum Spanning Tree (MST) was adopted and implemented in order to avoid methodological biases in graph comparisons and retain only the strongest connections per network. Subsequently, six global and three local metrics were calculated in order to retrieve useful features concerning the characteristics of each MST. Moreover, the local metrics of degree and betweenness centrality were used to detect hubs, i.e., nodes with high importance. The computed set of metrics gave rise to extensive statistical analysis in order to examine differences between low and high-IQ groups, as well as between all possible gender-based group combinations. Our results reveal that both male and female networks have small-world properties with differences in females (especially in higher IQ females) indicative of higher neural efficiency in cerebellum. There is a trend toward the same direction in men, but without significant differences. Finally, three lobules showed maximum correlation with the median response time in low-IQ individuals, implying that there is an increased effort dedicated locally by this population in cognitive tasks

Addressing the clinical unmet needs in primary Sjögren's Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts

For many decades, the clinical unmet needs of primary Sjögren's Syndrome (pSS) have been left unresolved due to the rareness of the disease and the complexity of the underlying pathogenic mechanisms, including the pSS-associated lymphomagenesis process. Here, we present the HarmonicSS cloud-computing exemplar which offers beyond the state-of-the-art data analytics services to address the pSS clinical unmet needs, including the development of lymphoma classification models and the identification of biomarkers for lymphomagenesis. The users of the platform have been able to successfully interlink, curate, and harmonize 21 regional, national, and international European cohorts of 7,551 pSS patients with respect to the ethical and legal issues for data sharing. Federated AI algorithms were trained across the harmonized databases, with reduced execution time complexity, yielding robust lymphoma classification models with 85% accuracy, 81.25% sensitivity, 85.4% specificity along with 5 biomarkers for lymphoma development. To our knowledge, this is the first GDPR compliant platform that provides federated AI services to address the pSS clinical unmet needs. © 2022 The Author(s

Towards a European Health Research and Innovation Cloud (HRIC)

The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe

Επεξεργασία και ανάλυση ιατρικών και άλλων συναφών δεδομένων μεγάλου όγκου

The reduced quality and the increased structural and conceptual heterogeneity of the clinical databases combined with the presence of data silos obscure the sharing and analysis of medical data. These open issues in healthcare leverage the development and secure deployment of robust and unbiased AI (Artificial Intelligence) workflows to address clinical unmet needs, including: (i) the development of robust disease classification and risk stratification models, (ii) the detection of new biomarkers, and (iii) the discovery of targeted therapies, among others. In this thesis, we aim to address the open issues and unmet needs in healthcare through the development of beyond the state of the art methods which are built on top of four main innovation areas: (i) Innovation Area 1 - data curation, where we propose a fully automated, efficient and scalable medical data curation workflow to enhance the quality of the diverse medical data including clinical and genetic data across multiple time-points, (ii) Innovation Area 2 - data harmonization, where we propose a hybrid, fully automated data harmonization workflow combining lexical and semantic analysis based on word embeddings which is built on top of external knowledge bases to overcome structural heterogeneities across clinical databases, (iii) Innovation Area 3 - synthetic data generation, where we propose a large-scale synthetic data generator to significantly enhance the statistical power of clinical databases with insufficient population size in order to enable the simulation of clinical trials, as well as, to enhance the classification performance of the existing AI models through data augmentation, and (iv) Innovation Area 4 – federated/distributed learning, where we propose a federated AI deployment framework which removes the need for the installation of local servers or any type of software in each site through the adoption of a federated AI modeling engine supporting a large family of federated AI algorithms yielding interpretable and explainable AI models. The proposed four stage workflow was evaluated across six different clinical domains, including autoimmune diseases (AD) and particularly in primary Sjogren’s Syndrome (pSS), hypertrophic cardiomyopathy (HCM), cardiovascular diseases (CVD), mental disorders (MD), systemic autoinflammatory diseases (SAIDs), and particularly Kawasaki disease (KD), and Coronavirus disease (COVID-19). The applicability of the proposed workflow was successfully demonstrated by: (i) enhancing the quality of the clinical and laboratory data in pSS, HCM, COVID-19, CVD, MD, KD, (ii) reducing the levels of structural and conceptual heterogeneity among the clinical and laboratory data in pSS, CVD, MD and at the same time enabling the evaluation of cross-domain data harmonization, (iii) producing high quality and large scale synthetic data for in silico clinical trials in HCM, (iv) augmenting the existing lymphoma classification models in pSS and HCM risk stratification models, and (v) producing robust AI models for lymphoma classification in pSS, the detection of biomarkers for lymphomagenesis, the detection of biomarkers for Kawasaki disease, HCM risk stratification, ICU admission and mortality classification in COVID-19.Η μειωμένη ποιότητα και η αυξημένη δομική και εννοιολογική ετερογένεια των κλινικών βάσεων δεδομένων παγκοσμίως σε συνδυασμό με την παρουσία silo δεδομένων δυσκολεύουν τον διαμοιρασμό, την διασύνδεση και την επικείμενη ανάλυση των ιατρικών δεδομένων. Αυτά τα ανοιχτά ζητήματα στον τομέα της υγείας αναδεικνύουν την ανάγκη τον σχεδιασμό και την ανάπτυξη ασφαλών και αμερόληπτων ροών εργασίας AI (Τεχνητή Νοημοσύνη) για την αντιμετώπιση κλινικών ανεκπλήρωτων αναγκών, όπως: (i) η ανάπτυξη ισχυρών μοντέλων ταξινόμησης ασθενειών και διαστρωμάτωσης κινδύνου, (ii) η ανίχνευση νέων βιοδεικτών, και (iii) η ανακάλυψη στοχευμένων θεραπειών, μεταξύ άλλων. Σε αυτή τη διατριβή, στοχεύουμε να αντιμετωπίσουμε τα ανοιχτά ζητήματα και τις ανεκπλήρωτες ανάγκες στον τομέα της υγείας μέσω της ανάπτυξης καινοτόμων μεθόδων και ροών εργασίας, οι οποίες δομήθηκαν γύρω από τέσσερις κύριους τομείς καινοτομίας: (i) Περιοχή Καινοτομίας 1 - Εξυγίανση δεδομένων (data curation), όπου προτείνουμε μια πλήρως αυτοματοποιημένη, αποτελεσματική και επεκτάσιμη ροή εργασιών εξυγίανσης των ιατρικών δεδομένων για τη βελτίωση της ποιότητας των ιατρικών δεδομένων, συμπεριλαμβανομένων των κλινικών και γενετικών δεδομένων σε πολλαπλά χρονικά σημεία, (ii) Τομέας Καινοτομίας 2 - εναρμόνιση δεδομένων (data harmonization), όπου προτείνουμε μια υβριδική και πλήρως αυτοματοποιημένη μέθοδο εναρμόνισης δεδομένων που συνδυάζει την λεκτική και την σημασιολογική ανάλυση βασισμένη σε ενσωματώσεις λέξεων, η οποία δομήθηκε γύρω από εξωτερικές βάσεις γνώσεων για να ξεπεραστούν οι δομικές και εννοιολογικές ετερογένειες σε κλινικές βάσεις δεδομένων, (iii) Τομέας Καινοτομίας 3 - παραγωγή συνθετικών δεδομένων (synthetic data generation), όπου προτείνουμε μια γεννήτρια μεγάλης κλίμακας συνθετικών δεδομένων με στόχο να ενισχύσει σημαντικά τη στατιστική ισχύ των κλινικών βάσεων δεδομένων με ανεπαρκές μέγεθος πληθυσμού, προκειμένου να καταστεί δυνατή η προσομοίωση κλινικών δοκιμών, καθώς και για τη βελτίωση της απόδοσης της ταξινόμησης των υφιστάμενων μοντέλων τεχνητής νοημοσύνης μέσω της επαύξησης δεδομένων και (iv) Τομέας Καινοτομίας 4 – κατανεμημένη μάθηση εντός και εκτός του νέφους (Federated/distributed learning), όπου προτείνουμε ένα πλαίσιο ανάπτυξης κατανεμημένων μοντέλων τεχνητής νοημοσύνης που καταργεί την ανάγκη εγκατάστασης τοπικών διακομιστών και την εγκατάσταση οποιουδήποτε είδους λογισμικού σε κάθε silo δεδομένων μέσω της υιοθέτησης μιας κατανεμημένης μηχανής μοντελοποίησης AI που υποστηρίζει μια μεγάλη οικογένεια κατανεμημένων αλγορίθμων τεχνητής νοημοσύνης που παράγουν ερμηνεύσιμα και επεξηγήσιμα μοντέλα τεχνητής νοημοσύνης. Η προτεινόμενη μεθοδολογία τεσσάρων σταδίων αξιολογήθηκε σε έξι διαφορετικούς κλινικούς τομείς, συμπεριλαμβανομένων των αυτοάνοσων νοσημάτων (AD) και συγκεκριμένα στο πρωτοπαθές σύνδρομο Sjögren (pSS), την υπερτροφική μυοκαρδιοπάθεια (HCM), τις καρδιαγγειακές παθήσεις (CVD), τις ψυχικές διαταραχές (MD), τις συστημικές αυτοφλεγμονώδεις νόσους (SAIDs) και συγκεκριμένα της νόσου Kawasaki (KD) και τέλος του COVID-19. Η κλινική και τεχνική απήχηση της προτεινόμενης μεθοδολογίας αποδείχθηκε επιτυχής δεδομένου ότι οδήγησε: (i) στην βελτίωση της ποιότητας των κλινικών και εργαστηριακών δεδομένων στις ασθένειες pSS, HCM, COVID-19, CVD, MD, KD, (ii) στην μείωση των επιπέδων δομικής και εννοιολογικής ετερογένειας μεταξύ κλινικών και εργαστηριακών δεδομένα στις ασθένειες pSS, CVD, MD και ταυτόχρονα επιτρέποντας την αξιολόγηση της εναρμόνισης δεδομένων μεταξύ τομέων, (iii) στην παραγωγή συνθετικών δεδομένων υψηλής ποιότητας και μεγάλης κλίμακας για κλινικές δοκιμές πυριτίου στην HCM, (iv) στην βελτίωση της απόδοσης των υπαρχόντων μοντέλων ταξινόμησης λεμφώματος και διαστρωμάτωσης κινδύνου στις ασθένειες pSS και HCM μέσω της τεχνικής επαύξησης των δεδομένων, και (v) στην παραγωγή ισχυρών μοντέλων AI για ταξινόμηση λεμφώματος σε ασθενείς με pSS, ανίχνευση βιοδεικτών για λεμφογένεση σε ασθενείς με pSS, στην ανίχνευση βιοδεικτών για τη νόσο Kawasaki, στην διαστρωμάτωση κινδύνου σε ασθενείς με HCM, στην πρόβλεψη εισαγωγής ασθενών με COVID-19 στη ΜΕΘ και στην πρόβλεψη της θνησιμότητας αυτών