Simplicial and Topological Descriptions of Human Brain Dynamics

0544003 - ÚI 2022 RIV US eng J - Journal Article
Billings, Jacob - Saggar, M. - Hlinka, Jaroslav - Keilholz, S. - Petri, G.
Simplicial and Topological Descriptions of Human Brain Dynamics.
Network Neuroscience. Roč. 5, č. 2 (2021), s. 549-568. ISSN 2472-1751. E-ISSN 2472-1751
Institutional support: RVO:67985807
Keywords : Functional connectivity * Time-varying functional connectivity * Topological data analysis * Persistent homology
OECD category: Neurosciences (including psychophysiology
Impact factor: 4.980, year: 2021
Method of publishing: Open access
http://dx.doi.org/10.1162/netn_a_00190

While brain imaging tools like functional magnetic resonance imaging (fMRI) afford measurements of whole-brain activity, it remains unclear how best to interpret patterns found amid the data's apparent self-organization. To clarify how patterns of brain activity support brain function, one might identify metric spaces that optimally distinguish brain states across experimentally defined conditions. Therefore, the present study considers the relative capacities of several metric spaces to disambiguate experimentally defined brain states. One fundamental metric space interprets fMRI data topographically, that is, as the vector of amplitudes of a multivariate signal, changing with time. Another perspective compares the brain's functional connectivity, that is, the similarity matrix computed between signals from different brain regions. More recently, metric spaces that consider the data's topology have become available. Such methods treat data as a sample drawn from an abstract geometric object. To recover the structure of that object, topological data analysis detects features that are invariant under continuous deformations (such as coordinate rotation and nodal misalignment). Moreover, the methods explicitly consider features that persist across multiple geometric scales. While, certainly, there are strengths and weaknesses of each brain dynamics metric space, we find that those that track topological features optimally distinguish experimentally defined brain states.
Permanent Link: http://hdl.handle.net/11104/0321076