CNS*2025 Florence

Brief Description: With the ever increasing amount of data acquired in neuroscience applications there is an essential need to develop computationally effective, robust, and interpretable data processing algorithms. Recent advancements in graph inference, topology, information theory and deep learning have shown promising results in analyzing biological/physiological data, as well as datasets acquired by intelligent agents. Combining elements from different disciplines of information theory, mathematics, and machine learning is paramount for developing the next generation of methods that will facilitate big data analysis under the realm of better understanding brain dynamics, as well as neuroinspired system dynamics in general. The goal of the workshop is to bring researchers working in data science, neuroscience, mathematics, and machine learning together to discuss challenges posed by analyzing multimodal data sets in neuroscience along with potential solutions, exchange ideas and present their latest work in designing and analyzing effective data processing algorithms. This workshop will serve as a great opportunity to discuss innovative future directions for neuroinspired processing of large amounts of data, while considering novel mathematical data models and computationally efficient learning algorithms.

Schedule:

9:00 - 9:40: Kathryn Hess, EPFL, Topological perspectives on the connectome
Abstract: Over the past decade or so, tools from algebraic topology have been shown to be very useful for the analysis and characterization of networks, in particular for exploring the relation of structure to function. I will describe some of these tools and illustrate their utility in neuroscience, primarily in the framework of a collaboration with the Blue Brain Project.

9:45 - 10:25: Moo Kyung Chung, University of Wisconsin, Topological Embedding of Dynamically Changing Brain Networks
Abstract: We introduce a novel topological framework for embedding time-varying brain networks into a low-dimensional space. Our Topological Embedding captures the evolving structure of functional connectivity by mapping dynamic birth and death values of topological features (connected components and cycles) into a 2D plane. Unlike traditional analyses that rely on synchronized time-points or direct comparisons of network matrices, our method aligns the dynamic behavior of brain networks through their underlying topological features, thus offering invariance to temporal misalignments and inter-subject variability. Using resting-state functional magnetic resonance images (rs-fMRI), we demonstrate that the topological embedding reveals stable 0D homological structures and fluctuating 1D cycles across time, which are further analyzed in the frequency domain through the Fourier Transform. The resulting topological spectrograms exhibit strong associations with age and cognitive traits, including fluid intelligence. This study establishes a robust and interpretable topological representation for the analysis of dynamically changing brain networks, with broad applicability in neuroscience and neuroimaging-based biomarker discovery. The talk is based on arXiv:2502.05814

10:30 - 11:00: Coffee Break

11:00 - 11:40: Anna Korzeniewska, Johns Hopkins University, 

11:45 - 12:25: Vasileios Maroulas, University of Tennessee Knoxville,