🍩 Database of Original & Non-Theoretical Uses of Topology
(found 3 matches in 0.001621s)
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Semantic Segmentation of Microscopic Neuroanatomical Data by Combining Topological Priors With Encoder–decoder Deep Networks (2020)
Samik Banerjee, Lucas Magee, Dingkang Wang, Xu Li, Bing-Xing Huo, Jaikishan Jayakumar, Katherine Matho, Meng-Kuan Lin, Keerthi Ram, Mohanasankar Sivaprakasam, Josh Huang, Yusu Wang, Partha P. MitraAbstract
Understanding of neuronal circuitry at cellular resolution within the brain has relied on neuron tracing methods that involve careful observation and interpretation by experienced neuroscientists. With recent developments in imaging and digitization, this approach is no longer feasible with the large-scale (terabyte to petabyte range) images. Machine-learning-based techniques, using deep networks, provide an efficient alternative to the problem. However, these methods rely on very large volumes of annotated images for training and have error rates that are too high for scientific data analysis, and thus requires a substantial volume of human-in-the-loop proofreading. Here we introduce a hybrid architecture combining prior structure in the form of topological data analysis methods, based on discrete Morse theory, with the best-in-class deep-net architectures for the neuronal connectivity analysis. We show significant performance gains using our hybrid architecture on detection of topological structure (for example, connectivity of neuronal processes and local intensity maxima on axons corresponding to synaptic swellings) with precision and recall close to 90% compared with human observers. We have adapted our architecture to a high-performance pipeline capable of semantic segmentation of light-microscopic whole-brain image data into a hierarchy of neuronal compartments. We expect that the hybrid architecture incorporating discrete Morse techniques into deep nets will generalize to other data domains. -
Revealing Brain Network Dynamics During the Emotional State of Suspense Using TDA (2025)
Astrid A. Olave, Jose A. Perea, Francisco GómezAbstract
Suspense is an affective state that is ubiquitous in human life, from art to quotidian events. However, little is known about the behavior of large-scale brain networks during suspenseful experiences. To address this question, we examined the continuous brain responses of participants watching a suspenseful movie, along with reported levels of suspense from an independent set of viewers. We employ sliding window analysis and Pearson correlation to measure functional connectivity states over time. Then, we use Mapper, a topological data analysis tool, to obtain a graphical representation that captures the dynamical transitions of the brain across states; this representation enables the anchoring of the topological characteristics of the combinatorial object with the measured suspense. Our analysis revealed changes in functional connectivity within and between the salience, fronto-parietal, and default networks associated with suspense. In particular, the functional connectivity between the salience and fronto-parietal networks increased with the level of suspense. In contrast, the connections of both networks with the default network decreased. Together, our findings reveal specific dynamical changes in functional connectivity at the network level associated with variation in suspense, and suggest topological data analysis as a potentially powerful tool for studying dynamic brain networks.Suspense is a complex emotional experience whose neural bases remain poorly understood. This study analyzes fMRI data from participants watching a suspenseful Alfred Hitchcock film to track time-evolving brain connectivity patterns. The Mapper algorithm represented moment-to-moment functional connectivity, transforming high-dimensional data into a graph that retains local detail and global shape. During heightened suspense, connectivity between salience and fronto-parietal control networks increases, whereas links with the default-mode network diminish. These shifts trace recurring paths through a finite set of connectivity states, offering a topological view of large-scale neural dynamics and clarifying how distributed networks coordinate during emotionally engaging experiences.Community Resources