Alessandro Crimi

Brain Graph networks between biomarkers, graph convolutional networks, and causality  

Brain Graph networks between biomarkers, graph convolutional networks, and causality


Brain connectivity refers to the approach of representing different aspects of the brain (structural connections, correlation of blood concentration, gene expressions, etc). As a biomarker, brain connectivity provides valuable insights into the brain. By mapping and analyzing the intricate network of connections between different brain regions, researchers can identify aberrant connectivity patterns associated with various neurological disorders such as Alzheimer’s disease, schizophrenia, and autism spectrum disorders. These findings aid in early detection, diagnosis, and monitoring of these conditions, allowing for targeted interventions and personalized treatment strategies.

Furthermore, the advent of graph convolutional networks has revolutionized the field of brain connectivity analysis. GCNs utilize graph theory and deep learning techniques to extract meaningful features from brain connectivity data. By leveraging the rich information encoded in brain networks, GCNs enable accurate classification, prediction, and understanding of brain-related phenomena. They have been successfully applied in tasks such as brain image segmentation, functional connectivity analysis, and brain network comparison, providing novel insights into the complex dynamics of the human brain.

Moreover. the complex and nonlinear nature of brain dynamics often makes it difficult to establish direct causal links. Researchers must carefully consider confounding factors, such as network topology and feedback loops, to ensure the validity of their models. Advanced statistical and computational methods, such as Bayesian networks and structural equation modeling, are employed to address these challenges. 


Dr. Alessandro Crimi is a biomedical engineer and health economist who alternated his career between neuroimaging and healthcare management in low-income countries.

He was born in Italy. After completing his studies in engineering at the University of Palermo, he obtained a PhD in machine learning applied for medical imaging by the University of Copenhagen,  and an MBA in healthcare management by the University of Basel.  

Alessandro worked as post-doctoral researcher at the French Institute for Research in Computer Science (INRIA), Technical School of Switzerland (ETH-Zurich), Italian Institute for Technology (IIT), and University Hospital of Zurich. In those institutes he made significant contributions in the field of computational neuroscience.   

The post-doctoral years at European institutes were alternated by period lived in Ghana and other Sub-Saharan countries where Dr. Crimi taught and carried out in-field projects about healthcare management. He taught for 8 years at the African Institute for Mathematical Science (AIMS) in Ghana and South Africa the course of machine learning in medicine, where he also supervised numerous MSc theses.

The projects he conducted in Sub-Saharan Africa were related to prenatal care, diabetes, malaria and HIV management using novel technologies as machine learning, image processing and social engineering.

Since 2021, Dr. Crimi moved to the Center for computational medicine in Poland, where is a principal investigator, he had to limit his engagements with AIMS to MSc thesis supervision.

Dr. Crimi is currently the head of the neuroimaging lab at Sano working on finding novel biomarkers related to brain diseases with new technologies as machine learning and quantum computing.

Moreover, he is currently involved in activities for technology transfer to train immigrant and women with children towards entrepreneurship.

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