About the project
Objective
The goal of this project is to better understand how the brain changes in diseases like Alzheimer and Parkinson. We study the brain’s wiring using a special type of MRI scan and look for patterns in how different regions are connected. By using advanced mathematical tools, we can detect subtle changes in these brain networks that are often missed by standard methods. These patterns may help us distinguish between healthy and diseased brains, and even identify early signs of disease before symptoms become clear. Ultimately, this research could lead to more accurate and earlier diagnosis, as well as a better understanding of how these diseases develop over time.
Background
Alzheimer’s and Parkinson’s diseases are the two most common neurodegenerative diseases, affecting more than 50 million people worldwide. They have a major impact not only on the patients, but also their families and healthcare systems. By improving early diagnosis through advancing methods to learn from complex brain connectivity patterns, this proposal has the potential to generate, in the future, benefits that extend beyond the scientific community, contributing both to societal well-being and to the sustainability of healthcare systems.
Tractography from diffusion magnetic resonance imaging (dMRI) reveals the wiring of the human brain but remains challenging to analyze due to its complexity and variability. Classic approaches, such as streamline count, fractional anisotropy, mean diffusivity, and tract length often capture pairwise relationships, but neglect the higher-order structural organization of the white matter network. As a result, complex and spatially distributed alterations that emerge early in neurodegenerative diseases can remain undetected.
Topological data analysis (TDA) and machine learning on graphs offer promising complementary tools to address this limitation. TDA provides tools to quantify the global and local topology of structural connectivity, such as loops, cycles, and motifs, that go beyond metrics accounting for pairwise interactions. Graph-based machine learning methods, such as graph neuronal network (GNN), can then leverage this richer representation to learn complex patterns of connectivity and distinguish between healthy and diseased brains.
About the Digital Futures Postdoc Fellow
Ilaria Carannante is a Computational Neuroscientist with a background in mathematics. She holds a PhD in Computer Science, with specialization in Computational Biology from KTH. Ilaria has always been passionate about science and mathematics, and her biggest career goal is to contribute expertise in neuroscience to advance our understanding of the human brain and to develop approaches for remedying disorders that impede its proper functioning.
During her PhD in Professor Jeanette Hellgren Kotaleski’s lab, she developed data-driven multiscale models of a brain region called Striatum. As a postdoctoral researcher in Professor Alain Destexhe’s group at CNRS in Paris, she expanded her work to larger-scale networks with a special focus on the Basal Ganglia (to which the striatum belongs). The next natural step in her research journey is to extend this multiscale approach to the human brain. For this reason in her current research she aims to leverage diffusion MRI, combined with topological data analysis and graph neural networks, to uncover higher-order patterns in brain connectivity.
In the long term, she aims to build data-driven whole-brain models to better understand brain function in health and Parkinson’s disease, with the ultimate goal of improving diagnosis and guiding targeted interventions.
Main supervisor
Martina Scolamiero, Associate Professor at the division of Algebra, Combinatorics, Geometry and Topology, KTH.
Co-supervisor
Rodrigo Moreno, Professor at the division of Biomedical Imaging, KTH.
