SPS BSI Webinar: End-to-end Stroke imaging analysis, using reservoir computing-based effective connectivity, and interpretable AI
Date: 20-June-2025
Time: 1:00 PM ET (New York Time)
Presenter: Dr. Alessandro Crimi
Meeting information:
Meeting number: 2864 961 4474
Password: YMpfbfzV635 (96732398 when dialing from a phone or video system)
Join by phone:
+1-415-655-0002 US Toll
Access code: 2864 961 4474
https://gsumeetings.webex.com/gsumeetings/j.php?MTID=mff05876328189c696029c8673535395a
Join us Friday, June 20th, 2025, at 1:00 PM ET for an exciting virtual talk by Dr. Alessandro Crimi entitled: “End-to-end Stroke imaging analysis, using reservoir computing-based effective connectivity, and interpretable AI” as part of the activities of the Brain Space Initiative, co-sponsored by the Center for Translational Research in Neuroimaging and Data Science (TReNDS) and the Data Science Initiative, IEEE Signal Processing Society.
Abstract
End-to-end Stroke imaging analysis, using reservoir computing-based effective connectivity, and interpretable AI
We present an end-to-end AI framework for directed graphs including explainable AI. This is a machine learning pipeline combining reservoir computing and directed graph analysis to model brain connectivity in stroke patients using MRI data. Effective connectivity is derived via reservoir computing, enabling the creation of directed graph representations. These graphs are classified using a directed graph convolutional network. Explainable AI tools provide insights into disrupted brain networks, elucidating biomarkers for stroke classification and enhancing clinical interpretability. This approach highlights the potential of machine learning to improve patient stratification in stroke and other brain diseases. The technical innovations are related to reservoir computing networks, directed graph analysis, and explainable AI of effective brain connectivity. Current efforts related to foundation model (similar to LLMs) will be also presented
Biography
Dr. Alessandro Crimi
Alessandro Crimi, after completing his studies in engineering at the university of Palermo (Italy), obtained the Ph.D. in machine learning applied for medical imaging from the University of Copenhagen, and an MBA in healthcare management from the University of Basel.
He is currently a professor at AGH Krakow and worked as a 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.
Dr. Crimi has also been involved in global health projects in sub-Saharan Africa.
Recommended Articles:
- Crimi, Alessandro, Luca Dodero, Fabio Sambataro, Vittorio Murino, and Diego Sona. “Structurally constrained effective brain connectivity.” NeuroImage 239 (2021): 118288. (Link to Paper).
- Ciezobka, Wojciech, Joan Falcó-Roget, Cemal Koba, and Alessandro Crimi. "End-to-end Stroke Imaging Analysis using Effective Connectivity and Interpretable Artificial intelligence." IEEE Access (2025). (Link to Paper).
- Wardynski, Marcin, Iacopo Iacopini, Giovanni Petri, Vito LaTora, and Alessandro Crimi. "Modeling the Spread of Misfolded Proteins in Alzheimer's Disease using Higher-Order Simplicial Complex Contagion." medRxiv (2025): 2025-02. (Link to Paper).
- Gherardini, Luca, Aleksandra Zajdel, Lorenzo Pini, and Alessandro Crimi. "Prediction of misfolded proteins spreading in Alzheimer’s disease using machine learning and spreading models." Cerebral Cortex 33, no. 24 (2023): 11471-11485. (Link to Paper).
